Abstract
In order to improve the efficiency and accuracy of manual vehicle paint defect detection, the computer vision technology and deep learning methods is used to achieve automatic detection of vehicle paint defects based on small samples in this study. The vehicle body paint defect image was collected in real time, and a new data enhancement algorithm was proposed to enhance the database for the over-fitting phenomenon caused by small sample data. Aiming at the defect characteristics inherent in vehicle paints, an improved MobileNet-SSD algorithm for automatic detection of paint defects is proposed by improving the feature layer of MobileNet-SSD network and optimizing the matching strategy of bounding box. The experimental results show that the improved MobileNet-SSD algorithm can detect the defects of six traditional body paint films with an accuracy rate of over 95%, which is 10% faster than the traditional SSD algorithm, and can realize real-time and accurate detection of body paint defects.
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