文献类型： 会议论文

第一作者： Le He

作者： Le He ¹ ; Jun Huang ² ; Xue Li ¹ ; Bolun Guan ³ ;

作者机构： 1.School of Computer Science and Computing, Anhui University of Technology, Ma’Anshan, China

2.School of Computer Science and Computing, Anhui University of Technology, Ma’Anshan, China|Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

3.Agricultural Economy and Information Research InstituteAnhui Academy of Agricultural Sciences, Hefei, China

关键词： Performance evaluation;Computational modeling;Feature extraction;Inference algorithms;Steel;Task analysis;Surface treatment

会议名称： Youth Academic Annual Conference of Chinese Association of Automation

主办单位：

页码： 1146-1151

摘要： Steel surface defect detection, with the goal of enhancing the quality of steel within the industry, is a vital process in steel production. Over the years, a number of techniques have been developed to meet the challenge of object detection. Nonetheless, designing lightweight models that can detect defects quickly and accurately remains challenging due to a significant amount of environmental interference and limited computational resources of the edge devices used in steel factories. In this paper, In this paper, we propose an improved Lightweight Detection Network(LDN) for steel surface defect detection. First, to extract features, the lightweight MobileNetV2 model is employed as the backbone. Second, to improve the detection accuracy, a feedback mechanism from the feature pyramid is added to the backbone network to integrate the features from shallow and deep layers. Additionally, the Efficient Intersection over Union(EIoU) loss is utilized to make the regression more accurate. The experimental results indicate that the method’s accuracy in recognition surpasses that of previous models, and the parameters and computational requirements have decreased significantly, leading to a rapid and precise detection of steel surface defects.

分类号： tp3