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22 November 2022 A steel surface defect detection algorithm based on improved YOLOv5
Yiping Chen, Yu Guo
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Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751N (2022) https://doi.org/10.1117/12.2659988
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China
Abstract
In order to solve the problem of steel surface defect detection, an improved algorithm based on YOLOv5 is proposed. EIOU loss is used to replace the original GIOU loss function, and the attention mechanism SE module is added to the network model to strengthen important characteristic channels. By setting different training parameters in the steel defect set for multiple rounds of testing, the results show that under different parameters, the improved YOLOv5s model can detect steel surface defects with the mAP value of 86.9%, which is 8.7% higher than the original model. Compared with traditional steel surface defect detection methods, the proposed algorithm can detect the types and locations of steel surface defects more accurately.
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Yiping Chen and Yu Guo "A steel surface defect detection algorithm based on improved YOLOv5", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751N (22 November 2022); https://doi.org/10.1117/12.2659988
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KEYWORDS
Data modeling
Defect detection
Detection and tracking algorithms
Inspection
Performance modeling
Target detection
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