Open Access iconOpen Access

ARTICLE

crossmark

YOLO-DD: Improved YOLOv5 for Defect Detection

Jinhai Wang1, Wei Wang1, Zongyin Zhang1, Xuemin Lin1, Jingxian Zhao1, Mingyou Chen1, Lufeng Luo2,*

1 School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China
2 School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528000, China

* Corresponding Author: Lufeng Luo. Email: email

Computers, Materials & Continua 2024, 78(1), 759-780. https://doi.org/10.32604/cmc.2023.041600

Abstract

As computer technology continues to advance, factories have increasingly higher demands for detecting defects. However, detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes. To address this issue, this paper proposes YOLO-DD, a defect detection model based on YOLOv5 that is effective and robust. To improve the feature extraction process and better capture global information, the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer (RDAT). Additionally, an Information Gap Filling Strategy (IGFS) is proposed to improve the fusion of features at different scales. The classic lightweight attention mechanism Squeeze-and-Excitation (SE) module is also incorporated into the neck section to enhance feature expression and improve the model’s performance. Experimental results on the NEU-DET dataset demonstrate that YOLO-DD achieves competitive results compared to state-of-the-art methods, with a 2.0% increase in accuracy compared to the original YOLOv5, achieving 82.41% accuracy and 38.25 FPS (frames per second). The model is also tested on a self-constructed fabric defect dataset, and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5, demonstrating its stability and generalization ability. The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Keywords


Cite This Article

J. Wang, W. Wang, Z. Zhang, X. Lin, J. Zhao et al., "Yolo-dd: improved yolov5 for defect detection," Computers, Materials & Continua, vol. 78, no.1, pp. 759–780, 2024.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 266

    View

  • 94

    Download

  • 0

    Like

Share Link