Abstract:

The paint defects that exist during the automotive painting process affect the overall appearance quality of the car. In response to the problems of missed inspection，low efficiency，and high implementation cost of traditional inspection schemes in manual inspection，a paint defect detection method based on the improved YOLOv7 algorithm is proposed. A dataset of automotive paint defects was constructed，consisting of 4023 images，including 5 types of automotive paint defects； In response to the problem of insufficient detection accuracy of YOLOv7 algorithm on small defects，GAM attention mechanism and SPPFCSPC module were introduced into the original network to improve the algorithm's ability to extract small defect features. At the same time，an improved ELAN module was used to improve the network structure to reduce the problem of small target information loss caused by deep network，ensuring that the network model is reduced while improving the recognition accuracy of small features； Based on the constructed dataset，the defect detection performance of different algorithms was tested and the effectiveness of the module was verified. The experimental results show that this method significantly improves the detection ability of small defects on paint surfaces，with an average detection accuracy of 88.9%，which is the highest detection accuracy compared to various algorithms.

Key words: vehicle engineering, automotive paint surface, defect detection, deep learning