Enhanced You Only Look Once X for surface defect detection of strip steel - Wu, Ruiqi; Zhou, Feng; Li, Nan; Liu, Haibo; Guo, Naihong; Wang, Rugang (Frontiers in Neurorobotics, 2022)
Using deep learning-based methods to detect surface defects in strip steel can reduce the impact of human factors and lower costs while maintaining accuracy and efficiency. The main problem with such an approach, however, is the inability to balance accuracy and speed. In addition, the low proportion of valid information and the lack of distinctive features result in a high rate of missed detection of small objects. In this paper, we propose a lightweight YOLOX surface defect detection network that utilizes a multi-scale feature fusion attention module (MFFAM) to enhance the detection of small objects. The backbone of this network is a lightweight CSP structure, which improves the detection speed without compromising accuracy. In MFFAM, different scale receptive fields are applied on the feature layers with different resolutions, and features are fused and then passed into the spatial channel attention module in parallel. MFFAM can significantly improve the detection accuracy of small objects. Compared with the initial YOLOX, the mAP and FPS were 81.21% and 82.87Hz, respectively, which was an improvement of 4.29% and 12.72Hz. In comparison with the existing methods, the proposed method has superior performance and practicality.