Lightweight Steel Surface Defect Detection Algorithm Based on Improved RetinaNet
WANG Weijia1, ZHANG Yu2, WANG Jinghua1, XU Yong1
1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055; 2. Intelligent Equipment Division, HBIS Digital Technology Co., Ltd., Shijiazhuang 053099
Abstract For the requirement of the practical application, the existing defect detection algorithms suffer from the problems of slow detection speed and low detection accuracy. To address these issues, a lightweight steel surface defect detection algorithm based on improved RetinaNet is proposed. Firstly, the original backbone network is replaced by a lightweight network, and a cross-stage-partial structure is introduced to achieve effective propagation and lightweighting of gradients. Then, depth-separable convolution is employed to replace the traditional convolutional layer to further reduce the number of parameters and improve the detection speed. To compensate for the decrease in model accuracy caused by lightweighting, a spatial pyramid pooling mechanism based on the cross-stage partial structure is designed. The detection accuracy of the model is effectively improved by feature fusion at different scales. Finally, experiments on NEU-DET dataset and the self-built HBIS dataset demonstrate the proposed algorithm reaches a faster detection speed and higher accuracy. Moreover, the corresponding hardware and software system meets the real-time online detection requirements of the production line and it has been put into service.
Corresponding Authors:
XU Yong, Ph.D., professor. His research interests include pa-ttern recognition and deep learning.
About author:: WANG Weijia, Master student. His research interests include computer vision and object detection. ZHANG Yu, Bachelor. His research interests include computer vision and defect detection. WANG Jinghua, Ph.D., associate profe-ssor. His research interests include computer vision and unsupervised learning.
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2024, Vol. 37 
