Abstract As a crucial component of product quality surveillance in the industrial manufacturing field, surface defect detection plays a pivotal role in achieving industrial automation. Although numerous defect detection methods have been widely used in steel production, complicated defect characteristics still severely affect the accuracy and efficiency of defect detection methods. To solve this problem, we propose a multi-path feature aggregation network that can significantly improve steel defect detection performance. Firstly, we design an efficient multi-path aggregation feature extraction module combining gradient path with data path propagation design paradigm as the backbone feature extraction unit based on YOLOv5. Secondly, in the branch path of the backbone, deformable convolution is introduced to expand the effective receptive field and enhance the capability of extracting irregularly distributed defect features. Furthermore, we propose a chained feature enhanced pooling module that refines feature information to obtain feature maps containing more complicated defect details in the pooling layer. Experimental results on the publicly available steel surface defect datasets show that our proposed model achieves a mean average precision of 81.9 % on Northeastern University-DET and 72.1 % on GC10-DET improving by 4.6 % and 3.4 % over the baseline, respectively. The proposed defect detection network can achieve a balance between accuracy and efficiency, and meet the demand in practical production. The detection speed of our work reaches 41.1 frames per second (FPS) and 40.0 FPS on the above two datasets, which demonstrates that our method has achieved comprehensive performance in steel surface defect detection.

Zhongyang Li

Publications