Year: 2025
Type: article
Abstract: With the rapid development of the automobile industry, the quality inspection of airbags, as a key component of automobile safe driving equipment, is crucial. However, the traditional manual quality inspection methods have problems such as low efficiency and susceptibility to human factors. In order to solve these problems, this paper proposes an airbag defect detection algorithm based on the improved YOLOv8. By using the SIoU loss function and optimizing the C2f module, the model's ability to detect defects on small targets is improved. The experimental results show that the improved algorithm achieves a mean accuracy (mAP) of 98.3% in detecting defects such as stylus bending, spring assembly misalignment, and snaps, which is 0.9 percentage points higher than that of the pre-improved YOLOv8 model, and the detection efficiency is significantly improved, which provides powerful technical support for the quality control of automobile manufacturing.
DOI: 10.54097/f2gv7335
Authors: Weijian Shao, Zhihua Hu, Taohuang Liu
Institution: Shanghai Polytechnic University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409637583