Defect detection of injection-molded parts based on improved-YOLOv5. Issue 1 (1st December 2022)

• Record Type: Journal Article
• Authors: Liang, Haoming; Chen, Jianrui; Xie, Wei; Yu, Xiaoyuan; Wu, Weilin
• Is Part Of: Journal of physics. Volume 2390 Issue 1(2022)
• Publication Date: 2022-12-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2390/1/012049
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Quality inspection with injection-molded parts (IMPs) is an essential part of the process of producing IMPs. To improve production quality, we propose an improved-YOLOv5 model to detect the object defection of the IMPs. Different from the original YOLOv5, we replace the coupled detection head (CH) with the independent decoupled head (IDCH), while adding the Squeeze-and-Excitation attention mechanism (SE) into the regression branch. Furthermore, a defective dataset of IMPs is constructed within the real-world production line, including 1357 IMPs images. The experimental results demonstrate the effectiveness of the improve-YOLOv5. Compared with YOLOv5, the precision and recall of the modified algorithm in this paper are increased by 2.5 % and 4.7 %, respectively. For difficult categories, precision and recall are increased by 8.7 % and 13.5 %, respectively.