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8 June 2023 Research on algorithm of surface defect detection of aluminum profile based on YOLO
Dong Lu, Kelimu Muhetae
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Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270719 (2023) https://doi.org/10.1117/12.2680972
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China
Abstract
At present, due to the complex texture, small defect and large scale span of some surface defects in aluminum profile surface defect identification, the detection accuracy is low and fails to meet the requirements of industrial detection. To solve these problems, an improved Yolov5 algorithm model is proposed. Based on the Yolov5 model, the BOA3 (Bottleneck PfAAM) module is used to replace the previous C3 module for small targets, which tend to improve the ability of high-level feature extraction, and reduce the number of references and the amount of computation. For multi-scale problems, the middle C3 module of the main trunk is Bottleneck changed to a double-branch structure, which tends to fuse characteristic information of different scales. Finally, the loss function CIOU is replaced by EIOU, and EIou-NMS is introduced to remove redundant boundary boxes and improve the inference accuracy of the model. Experimental results show that the detection accuracy of the improved Yolov5s model is 9.8% higher than that of the original model, which is superior to the latest Yolov7 model.
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Dong Lu and Kelimu Muhetae "Research on algorithm of surface defect detection of aluminum profile based on YOLO", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270719 (8 June 2023); https://doi.org/10.1117/12.2680972
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KEYWORDS
Aluminum
Detection and tracking algorithms
Defect detection
Small targets
Target detection
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