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
1. School of Automation, Nanjing Institute of Technology, Nanjing, Jiangsu 211167,China
2. Jiangsu Province Engineering Research Center of Intelli Sence Technology and System, Nanjing, Jiangsu 211167,China
3. Wuxi Institute of Metrology and Testing, Wuxi, Jiangsu 214000,China
4. Nanjing Institute of Measurement and Testing Technology, Nanjing, Jiangsu 210049,China
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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2022, Vol. 43 
