A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference
•An HLEDNet was presented for concrete bridge crack detection and measurement.•The lightweight design was integrated with HLEDNet backbone architecture.•The influence of annotating handwriting interference was highlighted.•Crack information was retrieved by considering the crack depth information.•T...
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| Vydáno v: | Measurement : journal of the International Measurement Confederation Ročník 216; s. 112892 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2023
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| ISSN: | 0263-2241, 1873-412X |
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| Abstract | •An HLEDNet was presented for concrete bridge crack detection and measurement.•The lightweight design was integrated with HLEDNet backbone architecture.•The influence of annotating handwriting interference was highlighted.•Crack information was retrieved by considering the crack depth information.•The unit conversion from pixel to mm was achieved for practical applications.
Although many learning-based studies have been conducted to detect cracks, there are still many problems in practice, such as slow inference speed due to a large number of hyperparameters required in network architectures and compromised detection accuracy in different environments. To address these issues, the current study employed a Hybrid Lightweight Encoder-Decoder Network (HLEDNet) as an ad-hoc crack segmentation and measurement system on real-world images captured from various concrete bridges. The proposed HLEDNet model was trained and tested with 3000 annotated images with further extensive data augmentation, which achieved 86.92%, 85.71%, 86.31, and 86.01% in precision, recall, F1 score, and mean intersection over union (mIoU), respectively. A crack measurement module was proposed using combined postprocessing techniques, where the R-squared values of the regression lines in crack length and average crack width are 0.9857 and 0.9925, respectively. Finally, an experimental study was undertaken to convert the crack measuring unit from pixel to millimetre. |
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| AbstractList | •An HLEDNet was presented for concrete bridge crack detection and measurement.•The lightweight design was integrated with HLEDNet backbone architecture.•The influence of annotating handwriting interference was highlighted.•Crack information was retrieved by considering the crack depth information.•The unit conversion from pixel to mm was achieved for practical applications.
Although many learning-based studies have been conducted to detect cracks, there are still many problems in practice, such as slow inference speed due to a large number of hyperparameters required in network architectures and compromised detection accuracy in different environments. To address these issues, the current study employed a Hybrid Lightweight Encoder-Decoder Network (HLEDNet) as an ad-hoc crack segmentation and measurement system on real-world images captured from various concrete bridges. The proposed HLEDNet model was trained and tested with 3000 annotated images with further extensive data augmentation, which achieved 86.92%, 85.71%, 86.31, and 86.01% in precision, recall, F1 score, and mean intersection over union (mIoU), respectively. A crack measurement module was proposed using combined postprocessing techniques, where the R-squared values of the regression lines in crack length and average crack width are 0.9857 and 0.9925, respectively. Finally, an experimental study was undertaken to convert the crack measuring unit from pixel to millimetre. |
| ArticleNumber | 112892 |
| Author | Lee, Vincent C.S. Deng, Jianghua Lu, Ye |
| Author_xml | – sequence: 1 givenname: Jianghua orcidid: 0000-0001-9280-8741 surname: Deng fullname: Deng, Jianghua email: dengjh@czust.edu.cn organization: Department of Structural Engineering, School of Civil Engineering and Architecture, Changzhou Institute of Technology, Changzhou 213032, China – sequence: 2 givenname: Ye orcidid: 0000-0002-2319-7681 surname: Lu fullname: Lu, Ye organization: Department of Civil Engineering, Monash University, Melbourne, Australia – sequence: 3 givenname: Vincent C.S. surname: Lee fullname: Lee, Vincent C.S. organization: Faculty of Information Technology, Monash University, Melbourne, Australia |
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| Cites_doi | 10.1016/j.autcon.2021.103831 10.1177/1475921718764873 10.1016/j.patrec.2008.04.005 10.1016/j.autcon.2020.103357 10.3390/s20072069 10.1016/j.autcon.2019.04.014 10.1016/j.autcon.2022.104364 10.1016/j.aei.2018.05.004 10.1016/j.conbuildmat.2020.119096 10.1016/j.measurement.2016.02.044 10.1016/j.istruc.2020.03.010 10.1016/j.tust.2018.04.002 10.1016/j.measurement.2020.108693 10.1109/TKDE.2009.191 10.1016/j.aei.2017.03.003 10.1111/mice.12334 10.1016/j.conbuildmat.2017.04.097 10.1016/j.autcon.2018.11.028 10.1111/mice.12421 10.1109/TPAMI.2017.2699184 10.3390/s21051688 10.1016/j.measurement.2021.109316 10.1016/j.autcon.2011.03.004 10.1109/TIE.2019.2945265 10.1111/mice.12622 10.1016/j.measurement.2021.109877 10.1016/j.autcon.2013.06.011 10.1016/j.autcon.2020.103382 10.1111/mice.12297 10.1016/j.conbuildmat.2019.117367 10.1061/(ASCE)CP.1943-5487.0000854 10.1016/j.measurement.2019.107377 10.1016/j.autcon.2020.103291 10.1109/ICIP.2019.8803154 10.1007/s11771-013-1775-5 10.1111/mice.12433 10.3390/s20071838 10.1016/j.aei.2020.101037 10.1016/j.measurement.2020.108077 10.1007/s11831-018-9263-6 10.1016/j.measurement.2021.109171 10.1111/mice.12319 10.1108/IJSI-06-2019-0061 10.1016/j.conbuildmat.2022.129238 10.1111/mice.12412 10.1088/0964-1726/22/3/035019 10.1016/j.aei.2020.101105 10.1016/j.aei.2015.01.008 10.1109/CVPR.2015.7298965 10.1002/ecj.10151 10.1007/s00138-011-0394-0 10.1016/j.autcon.2019.103019 10.1016/j.aei.2020.101206 10.1016/j.measurement.2020.108698 10.1016/j.measurement.2021.110641 10.1016/j.eng.2018.11.030 10.1109/CVPR.2017.549 10.1177/1369433220986637 |
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| Keywords | Automated inspection Structural health monitoring Crack measurement Encoder-decoder architecture Atrous convolutions Concrete bridge cracks |
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| References | Żarski, Wójcik, Książek, Miszczak (b0310) 2021 Deng, Singh, Zhou, Lu, Lee (b0110) 2022; 356 Won, Sim (b0090) 2020; 20 D. Andrushia A, N. Anand, E. Lubloy, G. Prince Arulraj, Deep learning based thermal crack detection on structural concrete exposed to elevated temperature, Adv. Struct. Eng. 24 (2021) 1896–1909. 10.1177/1369433220986637. Jin, Lee, Hong (b0095) 2020; 110 Li, Zhao, Zhou (b0150) 2019; 34 Huang, Sun, Xue, Wang (b0035) 2017; 32 Jahanshahi, Masri (b0050) 2013; 22 Andrushia, Anand, Arulraj (b0085) 2020; 11 G. Lin, A. Milan, C. Shen, I. Reid, RefineNet: Multi-path refinement networks for high-resolution semantic segmentation, in: Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017. 2017-Janua, 2017, pp. 5168–5177. 10.1109/CVPR.2017.549. Kim, Cho (b0265) 2019; 26 Ni, Zhang, Chen (b0225) 2019; 34 Li, Bao, Xu, Shu, Zhou, Du, Wang, Zhang (b0320) 2022; 188 Zhang, Wang, Li, Yang, Dai, Peng, Fei, Liu, Li, Chen (b0220) 2017; 32 Park, Eem, Jeon (b0300) 2020; 252 Xu, Zhang (b0045) 2013; 20 Sun, Qian (b0025) 2016; 86 Ren, Huang, Hong, Lu, Yin, Zou, Shen (b0245) 2020; 234 T.G. Mondal, M.R. Jahanshahi, Autonomous vision-based damage chronology for spatiotemporal condition assessment of civil infrastructure using unmanned aerial vehicle, Smart Struct. Syst. 25 (2020) 733–749. 10.12989/sss.2020.25.6.733. Gao, Yuan, Tong, Yang, Yu (b0165) 2020; 164 Redmon, Divvala, Girshick, Farhadi (b0180) 2016 Andrushia, Thangarajan (b0030) 2017; 42 Xu, Li, Xie, Wu, Wang (b0270) 2021; 178 Liu, Yang, Lau, Wang, Luo, Lee, Ding (b0190) 2020; 35 . Pan, Yang (b0305) 2010; 22 Weng, Huang, Wang (b0080) 2019; 105 Andrushia, Anand, Neebha, Naser, Lubloy (b0280) 2022; 140 D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, in: ArXiv Prepr. ArXiv1412.6980, 2014. Simonyan, Zisserman (b0335) 2014; ArXiv1409.1556 Y. Wang, Q. Zhou, J. Liu, J. Xiong, G. Gao, X. Wu, L.J. Latecki, Lednet: A lightweight encoder-decoder network for real-time semantic segmentation, in: Proc. - Int. Conf. Image Process. ICIP. 2019-Septe, 2019, pp. 1860–1864. 10.1109/ICIP.2019.8803154. Choi, Cha (b0250) 2020; 67 Hüthwohl, Brilakis (b0115) 2018; 37 Yamaguchi, Hashimoto (b0055) 2009; 92 J. Deng, Y. Lu, V.C.S. Lee, Image-based crack identification for concrete bridges using region-based convolutional neural network, in: 9th Int. Conf. Struct. Heal. Monit. Intell. Infrastruct. Transf. Res. into Pract. SHMII 2019 - Conf. Proc., 2019. Hou, Dong, Wang, Wu (b0315) 2020; 119 Brostow, Fauqueur, Cipolla (b0350) 2009; 30 Deng, Lu, Lee (b0185) 2021; 20 Spencer, Hoskere, Narazaki (b0125) 2019; 5 I. Goodfellow, Y. Bengio, A. Courville, Deep learning, 2017. Deng, Lu, Lee (b0210) 2020; 35 Li, Li, Zhou, Liu, Ren (b0255) 2021; 176 J. Long, E. Shelhamer, T. Darrell, Fully Convolutional Networks for Semantic Segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 3431–3440. Yang, Li, Yu, Luo, Huang, Yang (b0015) 2018; 33 Zhu, German, Brilakis (b0060) 2011; 20 Wang, Zhang, Wang, Braham, Qiu (b0070) 2018; 33 Koch, Georgieva, Kasireddy, Akinci, Fieguth (b0010) 2015; 29 Diana Andrushia, Anand, Prince Arulraj (b0100) 2020; 27 Zhang, Li, Jia, Ma, Luo, Li (b0345) 2020; 152 Ali, Alnajjar, Al Jassmi, Gochoo, Khan, Serhani (b0140) 2021; 21 Pan, Zhang, Zhang (b0285) 2020; 119 F. Yu, V. Koltun, Multi-Scale Context Aggregation by Dilated Convolutions, in: ArXiv Prepr. ArXiv1511.07122, 2016: p. arXiv Prepr. arXiv1511.07122. Wang, Liu, Zheng, Yang, Zou (b0135) 2020; 43 Ali, Kang, Suh, Cha (b0200) 2021; 130 H. wei Huang, Q. tong Li, D. ming Zhang, Deep learning based image recognition for crack and leakage defects of metro shield tunnel, Tunn. Undergr. Sp. Technol. 77 (2018) 166–176. 10.1016/j.tust.2018.04.002. Adhikari, Moselhi, Bagchi (b0005) 2014; 39 Li, Xie, Gong, Yu, Xu, Sun, Wang (b0175) 2021; 47 Kang, Benipal, Gopal, Cha (b0295) 2020; 118 Alipour, Harris, Miller (b0230) 2019; 33 Chen, Zhu, Papandreou, Schroff, Adam (b0375) 2018 Xu, Bao, Chen, Zuo, Li (b0205) 2019; 18 Li, Liu, Ren, Qiao, Ma, Wan (b0290) 2021; 170 Wang, Song, Liu, Dong, Yan, Jiang (b0260) 2021; 170 Lee, Hwang, Choi, Choi (b0240) 2020; 27 Chow, Su, Wu, Tan, Mao, Wang (b0155) 2020; 45 Jahanshahi, Masri, Padgett, Sukhatme (b0065) 2013; 24 Tong, Gao, Zhang (b0215) 2017; 146 Cha, Choi, Suh, Mahmoudkhani, Büyüköztürk (b0160) 2018; 33 Nigam, Singh (b0040) 2020; 25 Chen, Liang, Gu, Zhang, Deng, Li (b0105) 2021; 184 Dung, Anh (b0235) 2019; 99 Kim, Yoon, Sim (b0020) 2020; 27 Zhang, Yuen (b0195) 2021 Feng, Zhang, Wang, Wang, Li (b0380) 2020; 20 Hoskere, Narazaki, Hoang, Spencer (b0145) 2018; ArXiv1805.01055 L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, in: IEEE Trans. Pattern Anal. Mach. Intell., 2018, pp. 834–848. 10.1109/TPAMI.2017.2699184. Payab, Abbasina, Khanzadi (b0075) 2019; 26 Xu (10.1016/j.measurement.2023.112892_b0205) 2019; 18 Jin (10.1016/j.measurement.2023.112892_b0095) 2020; 110 10.1016/j.measurement.2023.112892_b0275 Pan (10.1016/j.measurement.2023.112892_b0285) 2020; 119 Huang (10.1016/j.measurement.2023.112892_b0035) 2017; 32 Li (10.1016/j.measurement.2023.112892_b0255) 2021; 176 Choi (10.1016/j.measurement.2023.112892_b0250) 2020; 67 Li (10.1016/j.measurement.2023.112892_b0290) 2021; 170 Simonyan (10.1016/j.measurement.2023.112892_b0335) 2014; ArXiv1409.1556 Jahanshahi (10.1016/j.measurement.2023.112892_b0065) 2013; 24 Park (10.1016/j.measurement.2023.112892_b0300) 2020; 252 Chen (10.1016/j.measurement.2023.112892_b0105) 2021; 184 Żarski (10.1016/j.measurement.2023.112892_b0310) 2021 Zhu (10.1016/j.measurement.2023.112892_b0060) 2011; 20 Nigam (10.1016/j.measurement.2023.112892_b0040) 2020; 25 Hou (10.1016/j.measurement.2023.112892_b0315) 2020; 119 10.1016/j.measurement.2023.112892_b0340 Kang (10.1016/j.measurement.2023.112892_b0295) 2020; 118 Feng (10.1016/j.measurement.2023.112892_b0380) 2020; 20 Liu (10.1016/j.measurement.2023.112892_b0190) 2020; 35 Adhikari (10.1016/j.measurement.2023.112892_b0005) 2014; 39 Li (10.1016/j.measurement.2023.112892_b0175) 2021; 47 Ren (10.1016/j.measurement.2023.112892_b0245) 2020; 234 Chow (10.1016/j.measurement.2023.112892_b0155) 2020; 45 Brostow (10.1016/j.measurement.2023.112892_b0350) 2009; 30 Deng (10.1016/j.measurement.2023.112892_b0110) 2022; 356 Li (10.1016/j.measurement.2023.112892_b0320) 2022; 188 Deng (10.1016/j.measurement.2023.112892_b0210) 2020; 35 Kim (10.1016/j.measurement.2023.112892_b0020) 2020; 27 Dung (10.1016/j.measurement.2023.112892_b0235) 2019; 99 Alipour (10.1016/j.measurement.2023.112892_b0230) 2019; 33 Hoskere (10.1016/j.measurement.2023.112892_b0145) 2018; ArXiv1805.01055 10.1016/j.measurement.2023.112892_b0130 10.1016/j.measurement.2023.112892_b0330 Andrushia (10.1016/j.measurement.2023.112892_b0085) 2020; 11 Andrushia (10.1016/j.measurement.2023.112892_b0280) 2022; 140 10.1016/j.measurement.2023.112892_b0170 10.1016/j.measurement.2023.112892_b0370 Sun (10.1016/j.measurement.2023.112892_b0025) 2016; 86 Yang (10.1016/j.measurement.2023.112892_b0015) 2018; 33 Chen (10.1016/j.measurement.2023.112892_b0375) 2018 Zhang (10.1016/j.measurement.2023.112892_b0195) 2021 Andrushia (10.1016/j.measurement.2023.112892_b0030) 2017; 42 Wang (10.1016/j.measurement.2023.112892_b0135) 2020; 43 Gao (10.1016/j.measurement.2023.112892_b0165) 2020; 164 Deng (10.1016/j.measurement.2023.112892_b0185) 2021; 20 10.1016/j.measurement.2023.112892_b0325 Tong (10.1016/j.measurement.2023.112892_b0215) 2017; 146 10.1016/j.measurement.2023.112892_b0365 Spencer (10.1016/j.measurement.2023.112892_b0125) 2019; 5 Koch (10.1016/j.measurement.2023.112892_b0010) 2015; 29 Hüthwohl (10.1016/j.measurement.2023.112892_b0115) 2018; 37 10.1016/j.measurement.2023.112892_b0360 10.1016/j.measurement.2023.112892_b0120 Xu (10.1016/j.measurement.2023.112892_b0045) 2013; 20 Ali (10.1016/j.measurement.2023.112892_b0140) 2021; 21 Cha (10.1016/j.measurement.2023.112892_b0160) 2018; 33 Yamaguchi (10.1016/j.measurement.2023.112892_b0055) 2009; 92 Redmon (10.1016/j.measurement.2023.112892_b0180) 2016 Xu (10.1016/j.measurement.2023.112892_b0270) 2021; 178 Lee (10.1016/j.measurement.2023.112892_b0240) 2020; 27 Kim (10.1016/j.measurement.2023.112892_b0265) 2019; 26 Diana Andrushia (10.1016/j.measurement.2023.112892_b0100) 2020; 27 Wang (10.1016/j.measurement.2023.112892_b0070) 2018; 33 Pan (10.1016/j.measurement.2023.112892_b0305) 2010; 22 Ali (10.1016/j.measurement.2023.112892_b0200) 2021; 130 Weng (10.1016/j.measurement.2023.112892_b0080) 2019; 105 Jahanshahi (10.1016/j.measurement.2023.112892_b0050) 2013; 22 Payab (10.1016/j.measurement.2023.112892_b0075) 2019; 26 Li (10.1016/j.measurement.2023.112892_b0150) 2019; 34 Ni (10.1016/j.measurement.2023.112892_b0225) 2019; 34 Zhang (10.1016/j.measurement.2023.112892_b0345) 2020; 152 Wang (10.1016/j.measurement.2023.112892_b0260) 2021; 170 Won (10.1016/j.measurement.2023.112892_b0090) 2020; 20 Zhang (10.1016/j.measurement.2023.112892_b0220) 2017; 32 10.1016/j.measurement.2023.112892_b0355 |
| References_xml | – volume: 26 year: 2019 ident: b0265 article-title: Image-based concrete crack assessment using mask and region-based convolutional neural network publication-title: Struct. Control Heal. Monit. – volume: 18 start-page: 653 year: 2019 end-page: 674 ident: b0205 article-title: Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images publication-title: Struct. Heal. Monit. – volume: 11 start-page: 395 year: 2020 end-page: 409 ident: b0085 article-title: Anisotropic diffusion based denoising on concrete images and surface crack segmentation publication-title: Int. J. Struct. Integr. – volume: 234 year: 2020 ident: b0245 article-title: Image-based concrete crack detection in tunnels using deep fully convolutional networks publication-title: Constr. Build. Mater. – reference: J. Long, E. Shelhamer, T. Darrell, Fully Convolutional Networks for Semantic Segmentation, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 3431–3440. – start-page: 779 year: 2016 end-page: 788 ident: b0180 article-title: You only look once: Unified, real-time object detection publication-title: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit – start-page: 801 year: 2018 end-page: 818 ident: b0375 article-title: Encoder-Decoder with Atrous Separable Convolution for Semantic publication-title: Eur. Conf. Comput. Vis. – volume: 27 start-page: 1 year: 2020 end-page: 20 ident: b0100 article-title: A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform publication-title: Struct. Control Heal. Monit. – volume: 21 start-page: 1 year: 2021 end-page: 22 ident: b0140 article-title: Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures publication-title: Sensors – reference: I. Goodfellow, Y. Bengio, A. Courville, Deep learning, 2017. – volume: 39 start-page: 180 year: 2014 end-page: 194 ident: b0005 article-title: Image-based retrieval of concrete crack properties for bridge inspection publication-title: Autom. Constr. – volume: 184 year: 2021 ident: b0105 article-title: An improved minimal path selection approach with new strategies for pavement crack segmentation publication-title: Measurement – volume: 99 start-page: 52 year: 2019 end-page: 58 ident: b0235 article-title: Autonomous concrete crack detection using deep fully convolutional neural network publication-title: Autom. Constr. – volume: 25 start-page: 436 year: 2020 end-page: 447 ident: b0040 article-title: Crack detection in a beam using wavelet transform and photographic measurements publication-title: Structures – volume: 252 year: 2020 ident: b0300 article-title: Concrete crack detection and quantification using deep learning and structured light publication-title: Constr. Build. Mater. – volume: 35 year: 2020 ident: b0210 article-title: Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network publication-title: Comput. Civ. Infrastruct. Eng. – volume: 32 start-page: 188 year: 2017 end-page: 201 ident: b0035 article-title: Inspection equipment study for subway tunnel defects by grey-scale image processing publication-title: Adv. Eng. Informatics. – volume: 86 start-page: 26 year: 2016 end-page: 40 ident: b0025 article-title: Multi-scale wavelet transform filtering of non-uniform pavement surface image background for automated pavement distress identification publication-title: Measurement – volume: 27 year: 2020 ident: b0240 article-title: Estimation of crack width based on shape-sensitive kernels and semantic segmentation publication-title: Struct. Control Heal. Monit. – volume: 152 year: 2020 ident: b0345 article-title: Machinery fault diagnosis with imbalanced data using deep generative adversarial networks publication-title: Measurement – volume: 33 start-page: 1090 year: 2018 end-page: 1109 ident: b0015 article-title: Automatic pixel-level crack detection and measurement using fully convolutional network publication-title: Comput. Civ. Infrastruct. Eng. – reference: J. Deng, Y. Lu, V.C.S. Lee, Image-based crack identification for concrete bridges using region-based convolutional neural network, in: 9th Int. Conf. Struct. Heal. Monit. Intell. Infrastruct. Transf. Res. into Pract. SHMII 2019 - Conf. Proc., 2019. – volume: 146 start-page: 775 year: 2017 end-page: 787 ident: b0215 article-title: Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks publication-title: Constr. Build. Mater. – volume: 20 year: 2021 ident: b0185 article-title: Imaging-based crack detection on concrete surfaces using You Only Look Once network publication-title: Struct. Heal. Monit. – volume: 119 year: 2020 ident: b0315 article-title: Inspection of surface defects on stay cables using a robot and transfer learning publication-title: Autom. Constr. – volume: 24 start-page: 227 year: 2013 end-page: 241 ident: b0065 article-title: An innovative methodology for detection and quantification of cracks through incorporation of depth perception publication-title: Mach. Vis. Appl. – volume: 42 start-page: 671 year: 2017 end-page: 685 ident: b0030 article-title: An efficient visual saliency detection model based on Ripplet transform publication-title: Sadhana - Acad. Proc. Eng. Sci. – volume: 92 start-page: 1 year: 2009 end-page: 12 ident: b0055 article-title: Practical image measurement of crack width for real concrete structure publication-title: Electron. Commun. Japan – start-page: 1 year: 2021 end-page: 17 ident: b0195 article-title: Crack detection using fusion features-based broad learning system and image processing publication-title: Comput. Civ. Infrastruct. Eng. – volume: 178 year: 2021 ident: b0270 article-title: Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN publication-title: Measurement – reference: L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, in: IEEE Trans. Pattern Anal. Mach. Intell., 2018, pp. 834–848. 10.1109/TPAMI.2017.2699184. – volume: 43 year: 2020 ident: b0135 article-title: A smart surface inspection system using faster R-CNN in cloud-edge computing environment publication-title: Adv. Eng. Inform. – volume: 119 year: 2020 ident: b0285 article-title: A spatial-channel hierarchical deep learning network for pixel-level automated crack detection publication-title: Autom. Constr. – reference: H. wei Huang, Q. tong Li, D. ming Zhang, Deep learning based image recognition for crack and leakage defects of metro shield tunnel, Tunn. Undergr. Sp. Technol. 77 (2018) 166–176. 10.1016/j.tust.2018.04.002. – volume: 33 start-page: 110 year: 2018 end-page: 123 ident: b0070 article-title: Pavement crack width measurement based on Laplace’s equation for continuity and unambiguity publication-title: Comput. Civ. Infrastruct. Eng. – volume: 45 year: 2020 ident: b0155 article-title: Anomaly detection of defects on concrete structures with the convolutional autoencoder publication-title: Adv. Eng. Inform. – volume: 176 year: 2021 ident: b0255 article-title: Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network publication-title: Measurement – reference: Y. Wang, Q. Zhou, J. Liu, J. Xiong, G. Gao, X. Wu, L.J. Latecki, Lednet: A lightweight encoder-decoder network for real-time semantic segmentation, in: Proc. - Int. Conf. Image Process. ICIP. 2019-Septe, 2019, pp. 1860–1864. 10.1109/ICIP.2019.8803154. – volume: 47 year: 2021 ident: b0175 article-title: Automatic defect detection of metro tunnel surfaces using a vision-based inspection system publication-title: Adv. Eng. Inform. – volume: ArXiv1409.1556 year: 2014 ident: b0335 article-title: Very deep convolutional networks for large-scale image recognition publication-title: ArXiv Prepr. – volume: 140 year: 2022 ident: b0280 article-title: Autonomous detection of concrete damage under fire conditions publication-title: Autom. Constr. – volume: 20 start-page: 874 year: 2011 end-page: 883 ident: b0060 article-title: Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation publication-title: Autom. Constr. – volume: 105 year: 2019 ident: b0080 article-title: Segment-based pavement crack quantification publication-title: Autom. Constr. – volume: 34 start-page: 616 year: 2019 end-page: 634 ident: b0150 article-title: Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network publication-title: Comput. Civ. Infrastruct. Eng. – reference: D. Andrushia A, N. Anand, E. Lubloy, G. Prince Arulraj, Deep learning based thermal crack detection on structural concrete exposed to elevated temperature, Adv. Struct. Eng. 24 (2021) 1896–1909. 10.1177/1369433220986637. – volume: 34 start-page: 367 year: 2019 end-page: 384 ident: b0225 article-title: Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning publication-title: Comput. Civ. Infrastruct. Eng. – volume: ArXiv1805.01055 year: 2018 ident: b0145 article-title: Vision-based structural inspection using multiscale deep convolutional neural networks publication-title: ArXiv Prepr. – volume: 32 start-page: 805 year: 2017 end-page: 819 ident: b0220 article-title: Automated pixel-level pavement crack detection on 3D Asphalt surfaces using a deep-learning network publication-title: Comput. Civ. Infrastruct. Eng. – volume: 26 start-page: 347 year: 2019 end-page: 365 ident: b0075 article-title: A brief review and a new graph-based image analysis for concrete crack quantification publication-title: Arch. Comput. Methods Eng. – volume: 5 start-page: 199 year: 2019 end-page: 222 ident: b0125 article-title: Advances in computer vision-based civil infrastructure inspection and monitoring publication-title: Engineering – volume: 33 start-page: 731 year: 2018 end-page: 747 ident: b0160 article-title: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types publication-title: Comput. Civ. Infrastruct. Eng. – volume: 170 year: 2021 ident: b0260 article-title: RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks publication-title: Measurement – volume: 20 year: 2020 ident: b0090 article-title: Automated transverse crack mapping system with optical sensors and big data analytics publication-title: Sensors (Switzerland) – reference: T.G. Mondal, M.R. Jahanshahi, Autonomous vision-based damage chronology for spatiotemporal condition assessment of civil infrastructure using unmanned aerial vehicle, Smart Struct. Syst. 25 (2020) 733–749. 10.12989/sss.2020.25.6.733. – volume: 33 start-page: 04019040 year: 2019 ident: b0230 article-title: Robust pixel-level crack detection using deep fully convolutional neural networks publication-title: J. Comput. Civ. Eng. – volume: 22 start-page: 1345 year: 2010 end-page: 1359 ident: b0305 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. – volume: 110 year: 2020 ident: b0095 article-title: A vision-based approach for autonomous crack width measurement with flexible kernel publication-title: Autom. Constr. – volume: 118 year: 2020 ident: b0295 article-title: Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning publication-title: Autom. Constr. – volume: 20 year: 2020 ident: b0380 article-title: Automatic pixel-level crack detection on dam surface using deep convolutional network publication-title: Sensors (Switzerland) – volume: 356 year: 2022 ident: b0110 article-title: Review on computer vision-based crack detection and quantification methodologies for civil structures publication-title: Constr. Build. Mater. – reference: D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, in: ArXiv Prepr. ArXiv1412.6980, 2014. – volume: 130 year: 2021 ident: b0200 article-title: Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures publication-title: Autom. Constr. – reference: F. Yu, V. Koltun, Multi-Scale Context Aggregation by Dilated Convolutions, in: ArXiv Prepr. ArXiv1511.07122, 2016: p. arXiv Prepr. arXiv1511.07122. – volume: 35 start-page: 1291 year: 2020 end-page: 1305 ident: b0190 article-title: Automated pavement crack detection and segmentation based on two-step convolutional neural network publication-title: Comput. Civ. Infrastruct. Eng. – volume: 30 start-page: 88 year: 2009 end-page: 97 ident: b0350 article-title: Semantic object classes in video:a high-definition ground truth database publication-title: Pattern Recognit. Lett. – reference: G. Lin, A. Milan, C. Shen, I. Reid, RefineNet: Multi-path refinement networks for high-resolution semantic segmentation, in: Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017. 2017-Janua, 2017, pp. 5168–5177. 10.1109/CVPR.2017.549. – volume: 170 year: 2021 ident: b0290 article-title: Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field publication-title: Measurement – reference: . – volume: 22 year: 2013 ident: b0050 article-title: A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation publication-title: Smart Mater. Struct. – volume: 20 start-page: 2605 year: 2013 end-page: 2613 ident: b0045 article-title: Crack detection of reinforced concrete bridge using video image publication-title: J. Cent. South Univ. – start-page: 1 year: 2021 end-page: 16 ident: b0310 article-title: Finicky transfer learning—a method of pruning convolutional neural networks for cracks classification on edge devices publication-title: Comput. Civ. Infrastruct. Eng. – volume: 67 start-page: 8016 year: 2020 end-page: 8025 ident: b0250 article-title: SDDNet: real-time crack segmentation publication-title: IEEE Trans. Ind. Electron. – volume: 37 start-page: 150 year: 2018 end-page: 162 ident: b0115 article-title: Detecting healthy concrete surfaces publication-title: Adv. Eng. Informatics. – volume: 27 year: 2020 ident: b0020 article-title: Automated bridge component recognition from point clouds using deep learning publication-title: Struct. Control Heal. Monit. – volume: 164 year: 2020 ident: b0165 article-title: Autonomous pavement distress detection using ground penetrating radar and region-based deep learning publication-title: Measurement – volume: 29 start-page: 196 year: 2015 end-page: 210 ident: b0010 article-title: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure publication-title: Adv. Eng. Inform. – volume: 188 year: 2022 ident: b0320 article-title: A deep residual neural network framework with transfer learning for concrete dams patch-level crack classification and weakly-supervised localization publication-title: Measurement – volume: 130 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0200 article-title: Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures publication-title: Autom. Constr. doi: 10.1016/j.autcon.2021.103831 – volume: 18 start-page: 653 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0205 article-title: Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images publication-title: Struct. Heal. Monit. doi: 10.1177/1475921718764873 – ident: 10.1016/j.measurement.2023.112892_b0340 – volume: 30 start-page: 88 year: 2009 ident: 10.1016/j.measurement.2023.112892_b0350 article-title: Semantic object classes in video:a high-definition ground truth database publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2008.04.005 – volume: 119 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0285 article-title: A spatial-channel hierarchical deep learning network for pixel-level automated crack detection publication-title: Autom. Constr. doi: 10.1016/j.autcon.2020.103357 – volume: 20 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0185 article-title: Imaging-based crack detection on concrete surfaces using You Only Look Once network publication-title: Struct. Heal. Monit. – volume: 20 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0380 article-title: Automatic pixel-level crack detection on dam surface using deep convolutional network publication-title: Sensors (Switzerland) doi: 10.3390/s20072069 – volume: 105 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0080 article-title: Segment-based pavement crack quantification publication-title: Autom. Constr. doi: 10.1016/j.autcon.2019.04.014 – ident: 10.1016/j.measurement.2023.112892_b0170 – volume: 140 year: 2022 ident: 10.1016/j.measurement.2023.112892_b0280 article-title: Autonomous detection of concrete damage under fire conditions publication-title: Autom. Constr. doi: 10.1016/j.autcon.2022.104364 – volume: 37 start-page: 150 year: 2018 ident: 10.1016/j.measurement.2023.112892_b0115 article-title: Detecting healthy concrete surfaces publication-title: Adv. Eng. Informatics. doi: 10.1016/j.aei.2018.05.004 – volume: 26 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0265 article-title: Image-based concrete crack assessment using mask and region-based convolutional neural network publication-title: Struct. Control Heal. Monit. – volume: 252 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0300 article-title: Concrete crack detection and quantification using deep learning and structured light publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2020.119096 – volume: 86 start-page: 26 year: 2016 ident: 10.1016/j.measurement.2023.112892_b0025 article-title: Multi-scale wavelet transform filtering of non-uniform pavement surface image background for automated pavement distress identification publication-title: Measurement doi: 10.1016/j.measurement.2016.02.044 – volume: 25 start-page: 436 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0040 article-title: Crack detection in a beam using wavelet transform and photographic measurements publication-title: Structures doi: 10.1016/j.istruc.2020.03.010 – start-page: 801 year: 2018 ident: 10.1016/j.measurement.2023.112892_b0375 article-title: Encoder-Decoder with Atrous Separable Convolution for Semantic – ident: 10.1016/j.measurement.2023.112892_b0120 doi: 10.1016/j.tust.2018.04.002 – volume: 170 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0290 article-title: Automatic recognition and analysis system of asphalt pavement cracks using interleaved low-rank group convolution hybrid deep network and SegNet fusing dense condition random field publication-title: Measurement doi: 10.1016/j.measurement.2020.108693 – volume: 35 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0210 article-title: Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network publication-title: Comput. Civ. Infrastruct. Eng. – volume: 22 start-page: 1345 year: 2010 ident: 10.1016/j.measurement.2023.112892_b0305 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – volume: ArXiv1409.1556 year: 2014 ident: 10.1016/j.measurement.2023.112892_b0335 article-title: Very deep convolutional networks for large-scale image recognition publication-title: ArXiv Prepr. – volume: ArXiv1805.01055 year: 2018 ident: 10.1016/j.measurement.2023.112892_b0145 article-title: Vision-based structural inspection using multiscale deep convolutional neural networks publication-title: ArXiv Prepr. – volume: 32 start-page: 188 year: 2017 ident: 10.1016/j.measurement.2023.112892_b0035 article-title: Inspection equipment study for subway tunnel defects by grey-scale image processing publication-title: Adv. Eng. Informatics. doi: 10.1016/j.aei.2017.03.003 – volume: 27 start-page: 1 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0100 article-title: A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform publication-title: Struct. Control Heal. Monit. – volume: 33 start-page: 731 year: 2018 ident: 10.1016/j.measurement.2023.112892_b0160 article-title: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12334 – volume: 146 start-page: 775 year: 2017 ident: 10.1016/j.measurement.2023.112892_b0215 article-title: Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2017.04.097 – volume: 99 start-page: 52 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0235 article-title: Autonomous concrete crack detection using deep fully convolutional neural network publication-title: Autom. Constr. doi: 10.1016/j.autcon.2018.11.028 – start-page: 1 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0310 article-title: Finicky transfer learning—a method of pruning convolutional neural networks for cracks classification on edge devices publication-title: Comput. Civ. Infrastruct. Eng. – volume: 34 start-page: 367 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0225 article-title: Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12421 – ident: 10.1016/j.measurement.2023.112892_b0360 doi: 10.1109/TPAMI.2017.2699184 – volume: 21 start-page: 1 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0140 article-title: Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures publication-title: Sensors doi: 10.3390/s21051688 – volume: 178 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0270 article-title: Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN publication-title: Measurement doi: 10.1016/j.measurement.2021.109316 – volume: 20 start-page: 874 year: 2011 ident: 10.1016/j.measurement.2023.112892_b0060 article-title: Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation publication-title: Autom. Constr. doi: 10.1016/j.autcon.2011.03.004 – volume: 67 start-page: 8016 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0250 article-title: SDDNet: real-time crack segmentation publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2019.2945265 – volume: 35 start-page: 1291 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0190 article-title: Automated pavement crack detection and segmentation based on two-step convolutional neural network publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12622 – volume: 184 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0105 article-title: An improved minimal path selection approach with new strategies for pavement crack segmentation publication-title: Measurement doi: 10.1016/j.measurement.2021.109877 – volume: 39 start-page: 180 year: 2014 ident: 10.1016/j.measurement.2023.112892_b0005 article-title: Image-based retrieval of concrete crack properties for bridge inspection publication-title: Autom. Constr. doi: 10.1016/j.autcon.2013.06.011 – volume: 119 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0315 article-title: Inspection of surface defects on stay cables using a robot and transfer learning publication-title: Autom. Constr. doi: 10.1016/j.autcon.2020.103382 – volume: 32 start-page: 805 year: 2017 ident: 10.1016/j.measurement.2023.112892_b0220 article-title: Automated pixel-level pavement crack detection on 3D Asphalt surfaces using a deep-learning network publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12297 – volume: 234 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0245 article-title: Image-based concrete crack detection in tunnels using deep fully convolutional networks publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2019.117367 – volume: 33 start-page: 04019040 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0230 article-title: Robust pixel-level crack detection using deep fully convolutional neural networks publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000854 – volume: 152 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0345 article-title: Machinery fault diagnosis with imbalanced data using deep generative adversarial networks publication-title: Measurement doi: 10.1016/j.measurement.2019.107377 – volume: 42 start-page: 671 year: 2017 ident: 10.1016/j.measurement.2023.112892_b0030 article-title: An efficient visual saliency detection model based on Ripplet transform publication-title: Sadhana - Acad. Proc. Eng. Sci. – ident: 10.1016/j.measurement.2023.112892_b0130 – volume: 118 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0295 article-title: Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning publication-title: Autom. Constr. doi: 10.1016/j.autcon.2020.103291 – ident: 10.1016/j.measurement.2023.112892_b0325 doi: 10.1109/ICIP.2019.8803154 – ident: 10.1016/j.measurement.2023.112892_b0365 – volume: 20 start-page: 2605 year: 2013 ident: 10.1016/j.measurement.2023.112892_b0045 article-title: Crack detection of reinforced concrete bridge using video image publication-title: J. Cent. South Univ. doi: 10.1007/s11771-013-1775-5 – volume: 34 start-page: 616 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0150 article-title: Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12433 – volume: 20 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0090 article-title: Automated transverse crack mapping system with optical sensors and big data analytics publication-title: Sensors (Switzerland) doi: 10.3390/s20071838 – volume: 27 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0240 article-title: Estimation of crack width based on shape-sensitive kernels and semantic segmentation publication-title: Struct. Control Heal. Monit. – volume: 43 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0135 article-title: A smart surface inspection system using faster R-CNN in cloud-edge computing environment publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2020.101037 – start-page: 1 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0195 article-title: Crack detection using fusion features-based broad learning system and image processing publication-title: Comput. Civ. Infrastruct. Eng. – volume: 164 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0165 article-title: Autonomous pavement distress detection using ground penetrating radar and region-based deep learning publication-title: Measurement doi: 10.1016/j.measurement.2020.108077 – volume: 26 start-page: 347 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0075 article-title: A brief review and a new graph-based image analysis for concrete crack quantification publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-018-9263-6 – volume: 176 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0255 article-title: Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network publication-title: Measurement doi: 10.1016/j.measurement.2021.109171 – volume: 33 start-page: 110 year: 2018 ident: 10.1016/j.measurement.2023.112892_b0070 article-title: Pavement crack width measurement based on Laplace’s equation for continuity and unambiguity publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12319 – ident: 10.1016/j.measurement.2023.112892_b0355 – volume: 11 start-page: 395 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0085 article-title: Anisotropic diffusion based denoising on concrete images and surface crack segmentation publication-title: Int. J. Struct. Integr. doi: 10.1108/IJSI-06-2019-0061 – volume: 356 year: 2022 ident: 10.1016/j.measurement.2023.112892_b0110 article-title: Review on computer vision-based crack detection and quantification methodologies for civil structures publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2022.129238 – volume: 33 start-page: 1090 year: 2018 ident: 10.1016/j.measurement.2023.112892_b0015 article-title: Automatic pixel-level crack detection and measurement using fully convolutional network publication-title: Comput. Civ. Infrastruct. Eng. doi: 10.1111/mice.12412 – volume: 22 year: 2013 ident: 10.1016/j.measurement.2023.112892_b0050 article-title: A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation publication-title: Smart Mater. Struct. doi: 10.1088/0964-1726/22/3/035019 – volume: 45 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0155 article-title: Anomaly detection of defects on concrete structures with the convolutional autoencoder publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2020.101105 – volume: 29 start-page: 196 year: 2015 ident: 10.1016/j.measurement.2023.112892_b0010 article-title: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2015.01.008 – ident: 10.1016/j.measurement.2023.112892_b0330 doi: 10.1109/CVPR.2015.7298965 – volume: 92 start-page: 1 year: 2009 ident: 10.1016/j.measurement.2023.112892_b0055 article-title: Practical image measurement of crack width for real concrete structure publication-title: Electron. Commun. Japan doi: 10.1002/ecj.10151 – volume: 24 start-page: 227 year: 2013 ident: 10.1016/j.measurement.2023.112892_b0065 article-title: An innovative methodology for detection and quantification of cracks through incorporation of depth perception publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-011-0394-0 – volume: 27 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0020 article-title: Automated bridge component recognition from point clouds using deep learning publication-title: Struct. Control Heal. Monit. – volume: 110 year: 2020 ident: 10.1016/j.measurement.2023.112892_b0095 article-title: A vision-based approach for autonomous crack width measurement with flexible kernel publication-title: Autom. Constr. doi: 10.1016/j.autcon.2019.103019 – start-page: 779 year: 2016 ident: 10.1016/j.measurement.2023.112892_b0180 article-title: You only look once: Unified, real-time object detection – volume: 47 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0175 article-title: Automatic defect detection of metro tunnel surfaces using a vision-based inspection system publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2020.101206 – volume: 170 year: 2021 ident: 10.1016/j.measurement.2023.112892_b0260 article-title: RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks publication-title: Measurement doi: 10.1016/j.measurement.2020.108698 – volume: 188 year: 2022 ident: 10.1016/j.measurement.2023.112892_b0320 article-title: A deep residual neural network framework with transfer learning for concrete dams patch-level crack classification and weakly-supervised localization publication-title: Measurement doi: 10.1016/j.measurement.2021.110641 – volume: 5 start-page: 199 year: 2019 ident: 10.1016/j.measurement.2023.112892_b0125 article-title: Advances in computer vision-based civil infrastructure inspection and monitoring publication-title: Engineering doi: 10.1016/j.eng.2018.11.030 – ident: 10.1016/j.measurement.2023.112892_b0370 doi: 10.1109/CVPR.2017.549 – ident: 10.1016/j.measurement.2023.112892_b0275 doi: 10.1177/1369433220986637 |
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| SubjectTerms | Atrous convolutions Automated inspection Concrete bridge cracks Crack measurement Encoder-decoder architecture Structural health monitoring |
| Title | A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference |
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