In this study, the structural problems of the YOLOv5 model were analyzed
emphatically. Based on the characteristics of fine defects in artificial
leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were
designed. These advancements led to the proposal of a high-performance
artificial leather fine defect detection model named YOLOD. YOLOD demonstrated
outstanding performance on the artificial leather defect dataset, achieving an
impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a
significant 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 of
YOLOD in both artificial leather defect detection and general object detection
tasks, making it a highly efficient and effective model for real-world
applications