基于DCE-YOLO算法的复杂背景下钢材表面缺陷检测研究OA

In the field of surface defect detection of steel materials,due to the variable scale and complex background of surface defects,problems such as missed detections,false detections and poor detection accuracy are often occur during detection.To ad-dress these issues,an innovative steel surface defect detection algorithm,named DCE-YOLO,is proposed.Firstly,a dual-path effi-cient downsampling is introduced,where the detail retention branch and the feature focusing branch work together to enhance the model's ability to determine the target location.Secondly,a multi-scale channel spatial self-attention mechanism is designed to enable the backbone network to extract key information more efficiently.Finally,an edge information enhancement module based on Sobel operator is constructed and embedded into C2f block to improve the ability of the model for capturing edges and textures.Experimental results on the NEU-DET dataset show that compared with the baseline model,the proposed DCE-YOLO model,with 2.91 ×106 parameters,improves mAP@0.5 and mAP@0.5:0.95 by 5.1％and 3.1％respectively.Additionally,the experimental results on the GC10-DET dataset confirm the robustness of this method.