基于CSE-YOLO的改进铝型材表面缺陷检测模型OA

To address the challenge of balancing model complexity,detection speed,and accuracy in aluminum profile surface defect detec-tion,an enhanced YOLOv5-based model named CSE-YOLO was developed.First,a novel CCFBiFPN architecture was constructed by inte-grating CCFF with BiFPN,which strengthened multi-scale feature fusion while reducing model complexity.Second,an SDI module incorporat-ing hybrid GSConv was implemented in the feature fusion stage to improve feature extraction capability.Finally,an ECA attention mechanism was embedded in the backbone network to enhance detection precision and overall performance.Experimental results demonstrated that com-pared with the baseline YOLOv5s model,the proposed approach achieved 5%and 1.3%improvements in mAP@0.5 and mAP@0.5:0.9 met-rics,respectively.Concurrently,parameter count and FLOPs were reduced by 4.3%and 4.4%,with the frame rate increasing from 140 to 143 FPS.The optimized architecture effectively balanced performance and complexity,demonstrating substantial practical value for industrial qual-ity inspection applications.