ISA: Ingenious Siamese Attention for object detection algorithms towards complex scenes
The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-ba...
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| Vydáno v: | ISA transactions Ročník 143; s. 205 - 220 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.12.2023
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| ISSN: | 0019-0578, 1879-2022, 1879-2022 |
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| Abstract | The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively. |
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| AbstractList | The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively.The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively. The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively. |
| Author | Ma, Xuemin Liu, Lianjun Dai, Yan Hu, Ziyu Deng, Pengwei |
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| Cites_doi | 10.1016/j.displa.2022.102322 10.1145/3424341 10.3390/s23031347 10.3390/electronics8080825 10.1016/j.displa.2021.102008 10.1109/CVPR42600.2020.00185 10.1109/ICCV48922.2021.00986 10.1109/CVPR.2017.690 10.1109/CVPR42600.2020.01155 10.1007/978-1-0716-0826-5_3 10.1109/ICCV.2017.322 10.1109/ICCV.2019.00086 10.1109/CVPR.2016.596 10.1109/CVPR42600.2020.00223 10.1007/s11263-014-0733-5 10.1109/CVPR.2014.81 10.1109/CVPR.2016.91 10.1109/ACCESS.2020.3007610 10.1109/CVPR.2018.00745 10.1007/978-3-030-01234-2_1 10.1109/TIM.2022.3216413 10.1109/ICCV.2015.169 10.1016/j.image.2022.116848 10.4304/jmm.6.1.14-21 10.1016/j.displa.2022.102317 10.1155/2021/5278820 10.1609/aaai.v36i2.20072 |
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| Keywords | Siamese network YOLO Complex scenes Object detection |
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| References | Liu (10.1016/j.isatra.2023.09.001_b5) 2016 10.1016/j.isatra.2023.09.001_b44 10.1016/j.isatra.2023.09.001_b45 10.1016/j.isatra.2023.09.001_b46 Lin (10.1016/j.isatra.2023.09.001_b17) 2014 Dai (10.1016/j.isatra.2023.09.001_b13) 2022 Ren (10.1016/j.isatra.2023.09.001_b26) 2015; Vol. 28 Jocher (10.1016/j.isatra.2023.09.001_b28) 2020 Tarel (10.1016/j.isatra.2023.09.001_b41) 2012; 4 10.1016/j.isatra.2023.09.001_b1 Hnewa (10.1016/j.isatra.2023.09.001_b8) 2021 Tan (10.1016/j.isatra.2023.09.001_b12) 2018 10.1016/j.isatra.2023.09.001_b3 Liu (10.1016/j.isatra.2023.09.001_b7) 2021; 68 Everingham (10.1016/j.isatra.2023.09.001_b16) 2015; 111 Chicco (10.1016/j.isatra.2023.09.001_b21) 2021 10.1016/j.isatra.2023.09.001_b4 Kvyetnyy (10.1016/j.isatra.2023.09.001_b35) 2017; Vol. 10445 Walambe (10.1016/j.isatra.2023.09.001_b31) 2021; 2021 Ge (10.1016/j.isatra.2023.09.001_b29) 2021 Dosovitskiy (10.1016/j.isatra.2023.09.001_b2) 2020 Katyal (10.1016/j.isatra.2023.09.001_b42) 2018 10.1016/j.isatra.2023.09.001_b22 Yang (10.1016/j.isatra.2023.09.001_b14) 2018 10.1016/j.isatra.2023.09.001_b23 10.1016/j.isatra.2023.09.001_b24 Yang (10.1016/j.isatra.2023.09.001_b51) 2021 Wu (10.1016/j.isatra.2023.09.001_b10) 2019 10.1016/j.isatra.2023.09.001_b20 Ronneberger (10.1016/j.isatra.2023.09.001_b34) 2015 Xu (10.1016/j.isatra.2023.09.001_b38) 2021; 17 Al Sobbahi (10.1016/j.isatra.2023.09.001_b36) 2022 Dey (10.1016/j.isatra.2023.09.001_b47) 2017 Redmon (10.1016/j.isatra.2023.09.001_b27) 2018 Huang (10.1016/j.isatra.2023.09.001_b6) 2019; 8 Wu (10.1016/j.isatra.2023.09.001_b37) 2022 Bochkovskiy (10.1016/j.isatra.2023.09.001_b19) 2020 Xiao (10.1016/j.isatra.2023.09.001_b39) 2020; 8 10.1016/j.isatra.2023.09.001_b15 He (10.1016/j.isatra.2023.09.001_b25) 2010; 33 Huang (10.1016/j.isatra.2023.09.001_b30) 2020; 43 Wang (10.1016/j.isatra.2023.09.001_b40) 2022; 71 10.1016/j.isatra.2023.09.001_b50 Dong (10.1016/j.isatra.2023.09.001_b32) 2011; 6 Li (10.1016/j.isatra.2023.09.001_b11) 2022 Shao (10.1016/j.isatra.2023.09.001_b18) 2018 Chowdhary (10.1016/j.isatra.2023.09.001_b33) 2021 Qiu (10.1016/j.isatra.2023.09.001_b43) 2023; 23 Hou (10.1016/j.isatra.2023.09.001_b9) 2022; 75 10.1016/j.isatra.2023.09.001_b48 10.1016/j.isatra.2023.09.001_b49 |
| References_xml | – start-page: 154 year: 2018 ident: 10.1016/j.isatra.2023.09.001_b42 article-title: Object detection in foggy conditions by fusion of saliency map and yolo – volume: 75 year: 2022 ident: 10.1016/j.isatra.2023.09.001_b9 article-title: Deformable pyramid R-CNN for 3D object detection (ChinaMM2022) publication-title: Displays doi: 10.1016/j.displa.2022.102322 – volume: 17 start-page: 1 issue: 1s year: 2021 ident: 10.1016/j.isatra.2023.09.001_b38 article-title: Exploring image enhancement for salient object detection in low light images publication-title: ACM Trans Multimedia Comput Commun Appl (TOMM) doi: 10.1145/3424341 – year: 2021 ident: 10.1016/j.isatra.2023.09.001_b33 – volume: Vol. 10445 start-page: 250 year: 2017 ident: 10.1016/j.isatra.2023.09.001_b35 article-title: Object detection in images with low light condition – year: 2022 ident: 10.1016/j.isatra.2023.09.001_b37 article-title: Edge computing driven low-light image dynamic enhancement for object detection publication-title: IEEE Trans Netw Sci Eng – volume: 23 start-page: 1347 issue: 3 year: 2023 ident: 10.1016/j.isatra.2023.09.001_b43 article-title: IDOD-YOLOV7: Image-dehazing YOLOV7 for object detection in low-light foggy traffic environments publication-title: Sensors doi: 10.3390/s23031347 – year: 2021 ident: 10.1016/j.isatra.2023.09.001_b29 – volume: 8 start-page: 825 issue: 8 year: 2019 ident: 10.1016/j.isatra.2023.09.001_b6 article-title: A rapid recognition method for electronic components based on the improved YOLO-V3 network publication-title: Electronics doi: 10.3390/electronics8080825 – year: 2017 ident: 10.1016/j.isatra.2023.09.001_b47 – volume: 68 year: 2021 ident: 10.1016/j.isatra.2023.09.001_b7 article-title: DLSE-net: A robust weakly supervised network for fabric defect detection publication-title: Displays doi: 10.1016/j.displa.2021.102008 – ident: 10.1016/j.isatra.2023.09.001_b24 doi: 10.1109/CVPR42600.2020.00185 – ident: 10.1016/j.isatra.2023.09.001_b1 doi: 10.1109/ICCV48922.2021.00986 – ident: 10.1016/j.isatra.2023.09.001_b46 doi: 10.1109/CVPR.2017.690 – ident: 10.1016/j.isatra.2023.09.001_b50 doi: 10.1109/CVPR42600.2020.01155 – start-page: 73 year: 2021 ident: 10.1016/j.isatra.2023.09.001_b21 article-title: Siamese neural networks: An overview publication-title: Artif Neural Netw doi: 10.1007/978-1-0716-0826-5_3 – year: 2018 ident: 10.1016/j.isatra.2023.09.001_b18 – ident: 10.1016/j.isatra.2023.09.001_b45 doi: 10.1109/ICCV.2017.322 – start-page: 234 year: 2015 ident: 10.1016/j.isatra.2023.09.001_b34 article-title: U-net: Convolutional networks for biomedical image segmentation – ident: 10.1016/j.isatra.2023.09.001_b48 doi: 10.1109/ICCV.2019.00086 – ident: 10.1016/j.isatra.2023.09.001_b15 doi: 10.1109/CVPR.2016.596 – volume: 33 start-page: 2341 issue: 12 year: 2010 ident: 10.1016/j.isatra.2023.09.001_b25 article-title: Single image haze removal using dark channel prior publication-title: IEEE Trans Pattern Anal Mach Intell – ident: 10.1016/j.isatra.2023.09.001_b23 doi: 10.1109/CVPR42600.2020.00223 – volume: 111 start-page: 98 year: 2015 ident: 10.1016/j.isatra.2023.09.001_b16 article-title: The pascal visual object classes challenge: A retrospective publication-title: Int J Comput Vis doi: 10.1007/s11263-014-0733-5 – ident: 10.1016/j.isatra.2023.09.001_b3 doi: 10.1109/CVPR.2014.81 – ident: 10.1016/j.isatra.2023.09.001_b4 doi: 10.1109/CVPR.2016.91 – volume: 43 start-page: 2623 issue: 8 year: 2020 ident: 10.1016/j.isatra.2023.09.001_b30 article-title: DSNet: Joint semantic learning for object detection in inclement weather conditions publication-title: IEEE Trans Pattern Anal Mach Intell – year: 2022 ident: 10.1016/j.isatra.2023.09.001_b11 – volume: 4 start-page: 6 issue: 2 year: 2012 ident: 10.1016/j.isatra.2023.09.001_b41 article-title: Vision enhancement in homogeneous and heterogeneous fog publication-title: IEEE Intell Transp Syst Mag – start-page: 3323 year: 2021 ident: 10.1016/j.isatra.2023.09.001_b8 article-title: Multiscale domain adaptive yolo for cross-domain object detection – year: 2020 ident: 10.1016/j.isatra.2023.09.001_b28 – volume: 8 start-page: 123075 year: 2020 ident: 10.1016/j.isatra.2023.09.001_b39 article-title: Making of night vision: Object detection under low-illumination publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3007610 – ident: 10.1016/j.isatra.2023.09.001_b49 doi: 10.1109/CVPR.2018.00745 – year: 2020 ident: 10.1016/j.isatra.2023.09.001_b19 – start-page: 11863 year: 2021 ident: 10.1016/j.isatra.2023.09.001_b51 article-title: Simam: A simple, parameter-free attention module for convolutional neural networks – year: 2020 ident: 10.1016/j.isatra.2023.09.001_b2 – ident: 10.1016/j.isatra.2023.09.001_b20 doi: 10.1007/978-3-030-01234-2_1 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.isatra.2023.09.001_b40 article-title: YOLOv5-fog: A multiobjective visual detection algorithm for fog driving scenes based on improved YOLOv5 publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2022.3216413 – volume: Vol. 28 year: 2015 ident: 10.1016/j.isatra.2023.09.001_b26 article-title: Faster r-cnn: Towards real-time object detection with region proposal networks – start-page: 740 year: 2014 ident: 10.1016/j.isatra.2023.09.001_b17 article-title: Microsoft coco: Common objects in context – start-page: 21 year: 2016 ident: 10.1016/j.isatra.2023.09.001_b5 article-title: Ssd: Single shot multibox detector – ident: 10.1016/j.isatra.2023.09.001_b44 doi: 10.1109/ICCV.2015.169 – year: 2022 ident: 10.1016/j.isatra.2023.09.001_b36 article-title: Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical evaluation, and challenges publication-title: Signal Process, Image Commun doi: 10.1016/j.image.2022.116848 – volume: 6 issue: 1 year: 2011 ident: 10.1016/j.isatra.2023.09.001_b32 article-title: Adaptive object detection and visibility improvement in foggy image publication-title: J Multimedia doi: 10.4304/jmm.6.1.14-21 – year: 2022 ident: 10.1016/j.isatra.2023.09.001_b13 article-title: A survey of detection-based video multi-object tracking publication-title: Displays doi: 10.1016/j.displa.2022.102317 – start-page: 1 year: 2018 ident: 10.1016/j.isatra.2023.09.001_b12 article-title: A multiple object tracking algorithm based on YOLO detection – year: 2018 ident: 10.1016/j.isatra.2023.09.001_b27 – start-page: 363 year: 2019 ident: 10.1016/j.isatra.2023.09.001_b10 article-title: Helmet detection based on improved YOLO V3 deep model – volume: 2021 year: 2021 ident: 10.1016/j.isatra.2023.09.001_b31 article-title: Lightweight object detection ensemble framework for autonomous vehicles in challenging weather conditions publication-title: Comput Intell Neurosci doi: 10.1155/2021/5278820 – ident: 10.1016/j.isatra.2023.09.001_b22 doi: 10.1609/aaai.v36i2.20072 – start-page: 221 year: 2018 ident: 10.1016/j.isatra.2023.09.001_b14 article-title: Real-time face detection based on YOLO |
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