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In this study, the structural problems of the YOLOv5 model were analyzedemphatically. Based on the characteristics of fine defects in artificialleather, four innovative structures, namely DFP, IFF, AMP, and EOS, weredesigned. These advancements led to the proposal of a high-performanceartificial leather fine defect detection model named YOLOD. YOLOD demonstratedoutstanding performance on the artificial leather defect dataset, achieving animpressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with asignificant reduction of 5.2% - 7.2% in the error detection rate. Moreover,YOLOD also exhibited remarkable performance on the general MS-COCO dataset,with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% -4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority ofYOLOD in both artificial leather defect detection and general object detectiontasks, making it a highly efficient and effective model for real-worldapplications.

High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection