Research on detection and classification of automotive body surface defects based on image processing and machine learning

Abstract The high noise in the automotive body surface image makes it difficult to extract defects. Moreover, a single feature cannot describe the complex automotive body surface defects leading to low classification accuracy. This paper proposes a highly robust method for classifying body surface defects. Firstly, an edge detection method that integrates the wavelet transform and the mathematical morphology is applied to detect defects. Subsequently, the geometric features of detected defects are combined with scale-invariant feature transform features to be the classification basis. Finally, the classification accomplishes through a support vector machine(SVM) with the parameters optimized via the grey wolf optimizer-SVM. Experimental results show the proposed classification method based on feature fusion achieves an average of 93% accuracy in automotive body surface defects classification and exhibits a 100% classification accuracy for pseudo-defects, which demonstrates the fusion of the wavelet transform and the mathematical morphology for automotive body surface defects detection can effectively reduce the impact of image noise for ensuring the extracted edges are intact.