Algorithm for automatic optimizing cross-cut saw based on computer vision techniques
The optimization of timber classification by grades and defect detection plays an important role in the production of timbers. Traditionally, a timber is manually cut by a worker according to his experience. Defect detection and classification of a timber are with great subjectivity. Meanwhile, the...
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| Veröffentlicht in: | Engineering Research Express Jg. 5; H. 4; S. 45022 - 45034 |
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01.12.2023
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| Abstract | The optimization of timber classification by grades and defect detection plays an important role in the production of timbers. Traditionally, a timber is manually cut by a worker according to his experience. Defect detection and classification of a timber are with great subjectivity. Meanwhile, the action is not safe enough. In this case, an automatic optimizing cross-cut saw to finish these tasks of timber classification by grades and defect detection is built significantly. Related algorithms and detailed procedures for optimizing cross-cut saws are proposed in this paper. Additionally, a vision system is used to capture images of a timber. Captured images are analyzed and processed. First, defects in these images are detected. Then the serviceable part (defect-free) of a timber can be determined. Based on the pretrained network, the timber can be classified. As the homography matrix has been known, the physical position can be confirmed. In our proposed system, the cutting list is transmitted from the industrial control computer to a motion control system, then the timber can be cut according to the cutting list automatically. In this paper, related algorithms and detailed procedures are given. Moreover, a new optimizing cross-cut saw is built. Experiments show that the processing time for each image is about 0.026s and the minimum mean average precision is 94.15%. In this case, it can make the optimizing cross-cut saw efficient, labor-saving and safe. Furthermore, related algorithms are suitable to improve a traditional automatic optimizing cross-cut saw. |
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| AbstractList | The optimization of timber classification by grades and defect detection plays an important role in the production of timbers. Traditionally, a timber is manually cut by a worker according to his experience. Defect detection and classification of a timber are with great subjectivity. Meanwhile, the action is not safe enough. In this case, an automatic optimizing cross-cut saw to finish these tasks of timber classification by grades and defect detection is built significantly. Related algorithms and detailed procedures for optimizing cross-cut saws are proposed in this paper. Additionally, a vision system is used to capture images of a timber. Captured images are analyzed and processed. First, defects in these images are detected. Then the serviceable part (defect-free) of a timber can be determined. Based on the pretrained network, the timber can be classified. As the homography matrix has been known, the physical position can be confirmed. In our proposed system, the cutting list is transmitted from the industrial control computer to a motion control system, then the timber can be cut according to the cutting list automatically. In this paper, related algorithms and detailed procedures are given. Moreover, a new optimizing cross-cut saw is built. Experiments show that the processing time for each image is about 0.026s and the minimum mean average precision is 94.15%. In this case, it can make the optimizing cross-cut saw efficient, labor-saving and safe. Furthermore, related algorithms are suitable to improve a traditional automatic optimizing cross-cut saw. |
| Author | Shao, Mingwei Ma, Hailong |
| Author_xml | – sequence: 1 givenname: Hailong orcidid: 0000-0003-3110-3945 surname: Ma fullname: Ma, Hailong organization: (Hechi University) Key Laboratory of AI and Information Processing , Education Department of Guangxi Zhuang Autonomous Region, Hechi University, Hechi, Guangxi, 546300, People’s Republic of China – sequence: 2 givenname: Mingwei surname: Shao fullname: Shao, Mingwei organization: University of Technology School of Information and Control Engineering, Qingdao , Qingdao 266000, People’s Republic of China |
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| Cites_doi | 10.1109/ACCESS.2023.3293864 10.1038/nature14539 10.1109/34.888718 10.1109/TPAMI.2016.2577031 10.1109/TSMC.1979.4310076 10.48550/arXiv.1911.09070 10.1007/s00226-016-0810-8 10.1145/3065386 10.48550/arXiv.1801.04381 10.1080/00207720802630685 10.1007/s00521-020-04819-5 10.1109/MSP.2018.2832195 10.48550/arXiv.1804.02767 10.1007/s10086-014-1410-6 10.3901/CJME.2010.03.375 10.1515/hf-2021-0051 |
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| References | Alex (erxacfb5bbib12) 2017; 60 Gonzalo (erxacfb5bbib4) 2009; 40 Hartley (erxacfb5bbib14) 2003 Li (erxacfb5bbib2) 2010; 23 Shao (erxacfb5bbib8) 2020; 56 Otsu (erxacfb5bbib10) 1979; 9 Yoshua (erxacfb5bbib11) 2015; 521 Breinig (erxacfb5bbib1) 2015; 61 Tan (erxacfb5bbib16) 2020 Chakravorty (erxacfb5bbib9) 2018; 35 García (erxacfb5bbib3) 2016; 50 Bouguet (erxacfb5bbib19) 2003 Ren (erxacfb5bbib13) 2016; 39 Chen (erxacfb5bbib6) 2020; 32 Han (erxacfb5bbib7) 2023; 11 Hwang (erxacfb5bbib5) 2021; 76 Sandler (erxacfb5bbib15) 2018; 2018 Redmon (erxacfb5bbib17) 2018 Zhang (erxacfb5bbib18) 2003; 22 |
| References_xml | – volume: 11 start-page: 71800 year: 2023 ident: erxacfb5bbib7 article-title: An improved YOLOv5 algorithm for wood defect detection based on attention publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3293864 – volume: 521 start-page: 436 year: 2015 ident: erxacfb5bbib11 article-title: Deep Learning publication-title: Nature doi: 10.1038/nature14539 – volume: 22 start-page: 1330 year: 2003 ident: erxacfb5bbib18 article-title: A flexible new technique for camera calibration publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.888718 – volume: 39 start-page: 1137 year: 2016 ident: erxacfb5bbib13 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2577031 – volume: 9 start-page: 62 year: 1979 ident: erxacfb5bbib10 article-title: A threshold selection method from gray-level histograms publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1979.4310076 – start-page: 1 year: 2020 ident: erxacfb5bbib16 article-title: Efficientdet: scalable and efficient object detection publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR) doi: 10.48550/arXiv.1911.09070 – volume: 50 start-page: 807 year: 2016 ident: erxacfb5bbib3 article-title: Eigenproblems in timber structural elements with uncertain properties publication-title: Wood Sci. Technol. doi: 10.1007/s00226-016-0810-8 – volume: 60 start-page: 84 year: 2017 ident: erxacfb5bbib12 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun. ACM doi: 10.1145/3065386 – volume: 2018 start-page: 4510 year: 2018 ident: erxacfb5bbib15 article-title: MobileNetV2: Inverted Residuals and Linear Bottlenecks publication-title: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) doi: 10.48550/arXiv.1801.04381 – volume: 40 start-page: 163 year: 2009 ident: erxacfb5bbib4 article-title: Automated visual inspection system for wood defect classification using computational intelligence techniques publication-title: Int. J. Syst. Sci. doi: 10.1080/00207720802630685 – volume: 32 start-page: 11229 year: 2020 ident: erxacfb5bbib6 article-title: A robust weakly supervised learning of deep Conv-Nets for surface defect inspection publication-title: Neural Computing and Applications doi: 10.1007/s00521-020-04819-5 – volume: 56 start-page: 123 year: 2020 ident: erxacfb5bbib8 article-title: A new algorithm for automatic optimizing cross-cut saw based on deep learning algorithm publication-title: Scientia Silvae Sinicae – volume: 35 start-page: 175 year: 2018 ident: erxacfb5bbib9 article-title: What is a signal? publication-title: IEEE Signal Process Mag. doi: 10.1109/MSP.2018.2832195 – year: 2003 ident: erxacfb5bbib19 – start-page: 1 year: 2018 ident: erxacfb5bbib17 article-title: YOLOv3: An Incremental Improvement publication-title: arXiv e-prints (2018) doi: 10.48550/arXiv.1804.02767 – volume: 61 start-page: 89 year: 2015 ident: erxacfb5bbib1 article-title: Classification of wood surfaces according to visual appearance by multivariate analysis of wood feature data publication-title: Journal of Wood Science doi: 10.1007/s10086-014-1410-6 – volume: 23 start-page: 375 year: 2010 ident: erxacfb5bbib2 article-title: Optimization algorithms for fully automatic optimizing cross-cut saw publication-title: Chin. J. Mech. Eng. doi: 10.3901/CJME.2010.03.375 – volume: 76 start-page: 1 year: 2021 ident: erxacfb5bbib5 article-title: Classification of wood knots using artificial neural networks with texture and local feature-based image descriptors publication-title: Holzforshung doi: 10.1515/hf-2021-0051 – year: 2003 ident: erxacfb5bbib14 |
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| SubjectTerms | classification computer vision deep learning defect detection image analysis |
| Title | Algorithm for automatic optimizing cross-cut saw based on computer vision techniques |
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