Improved YOLOv3 Algorithm for Surface Defect Detection of Aluminum Profile

YAO Bo; WEN Xiu-lan; JIAO Liang-bao; WANG Shu-gang; QIAN Zheng; LI Zi-kang

doi:10.3969/j.issn.1000-1158.2022.10.03

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Acta Metrologica Sinica ›› 2022, Vol. 43 ›› Issue (10) : 1256-1261. DOI: 10.3969/j.issn.1000-1158.2022.10.03

Improved YOLOv3 Algorithm for Surface Defect Detection of Aluminum Profile

YAO Bo¹,WEN Xiu-lan¹,JIAO Liang-bao²,WANG Shu-gang³,QIAN Zheng⁴,LI Zi-kang¹

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Abstract

Aiming at the problems of low accuracy and low efficiency in the detection of surface defects of aluminum profile, an improved method based on YOLOv3 is proposed. Firstly, the k-means clustering algorithm is used to cluster the collected data sets, and the target candidate box with the optimal size is selected. Then, considering the large surface defects of aluminum profile, the network hierarchical structure of YOLOv3 is adjusted. Six CBL units before the target detection layer are changed into four CBL units, and two residual units are added to improve the reuse of features. Compared with the classical convolution network Faster-RCNN and SSD, a large number of experimental results show that the accuracy of the proposed algorithm can reach 97%, and the detection speed can reach 47 frame/s. The proposed method is obviously better than Faster-RCNN and SSD, which is suitable for the aluminum profile surface defect detection with high accuracy and rapidity.

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metrology / surface defect detection / aluminum profile / deep learning / YOLOv3 method / k-means clustering

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YAO Bo,WEN Xiu-lan,JIAO Liang-bao,WANG Shu-gang,QIAN Zheng,LI Zi-kang. Improved YOLOv3 Algorithm for Surface Defect Detection of Aluminum Profile[J]. Acta Metrologica Sinica. 2022, 43(10): 1256-1261 https://doi.org/10.3969/j.issn.1000-1158.2022.10.03

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