Paint defect detection system based on improved algorithm

Paint quality is one of the important standards for measuring the overall quality of the product. Paint defects affect the appearance of the product, which will also expose the product to security risks, such as metal parts exposure corrosion, surface damage, and so on. Traditional paint detection methods mainly rely on manual visual inspection, which has low detection efficiency, low accuracy, and high influence by human factors. This project combines machine vision and deep learning technology and builds a paint defect data set covering eight classes of paint surface defects to train a paint surface deficiency detection model. By constructing the dataset with different backgrounds and optimizing the image processing algorithm before training, the model of multi-target and high-precision automatic identification and classification of paint defects in complex scenes was realized. On the test data set, eight classes of paint defects were detected with 93% accuracy.