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.

翻译：本研究重点分析了YOLOv5模型的结构性问题，并基于人造革精细缺陷的特点，设计了DFP、IFF、AMP和EOS四种创新结构。基于这些改进，提出了一种名为YOLOD的高性能人造革精细缺陷检测模型。YOLOD在人造革缺陷数据集上表现出色，与YOLOv5相比，AP_50提升了11.7%至13.5%，同时误检率显著降低了5.2%至7.2%。此外，YOLOD在通用MS-COCO数据集上也展现出卓越性能，与YOLOv5相比，AP提升了0.4%至2.6%，AP_S提升了2.5%至4.1%。这些结果证明了YOLOD在人造革缺陷检测与通用目标检测任务中的优越性，使其成为面向实际应用的高效模型。