In-depth Application of YOLOv8 Algorithm Improvement in Automotive Airbag Defect Detection

Weijian Shao; Zhihua Hu; Taohuang Liu

doi:10.54097/f2gv7335

Authors

Weijian Shao
Zhihua Hu
Taohuang Liu

DOI:

https://doi.org/10.54097/f2gv7335

Keywords:

Airbag; Defect detection; Deep learning; Target detection.

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.

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