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

TL;DR

This paper introduces YOLOD, a high-performance object detection model tailored for fine defect detection in artificial leather, achieving significant improvements over YOLOv5 in accuracy and error reduction, with strong generalization to standard datasets.

Contribution

The paper designs four innovative structures (DFP, IFF, AMP, EOS) to enhance YOLOv5, resulting in YOLOD, a model with superior detection performance for fine defects in artificial leather.

Findings

YOLOD improves AP_50 by 11.7%-13.5% on artificial leather dataset.

YOLOD reduces error detection rate by 5.2%-7.2%.

YOLOD outperforms YOLOv5 on MS-COCO with higher AP and AP_S.

Abstract

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.