Optimized YOLOv5 and Spatial Pyramid Pooling to Enhance Industrial Defect Detection

This study addresses industrial appearance defect detection challenges by enhancing the YOLOv5 algorithm using the NEU-DET dataset. Leveraging optimized Spatial Pyramid Pooling (SPPCSPS), the model exhibits improved accuracy in recognizing various defect types, mitigating issues of omission and misdetection. Experimental results demonstrate a 1.94% average accuracy improvement over the original YOLOv5. The proposed modifications enhance the algorithm's ability to detect defects, contributing to more accurate and efficient industrial inspection processes.