Abstract
Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms.
Highlights
Surface defect detection is an important part of industrial production, and has significant impact upon the quality of industrial products on the market
The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features
A large number of surface defect detection algorithms have emerged. These algorithms can be roughly classified into three categories: Traditional methods based on image structure features, methods combining statistical features with machine learning, and deep learning methods based on the Convolutional Neural Network (CNN)
Summary
Surface defect detection is an important part of industrial production, and has significant impact upon the quality of industrial products on the market. A large number of surface defect detection algorithms have emerged These algorithms can be roughly classified into three categories: Traditional methods based on image structure features, methods combining statistical features with machine learning, and deep learning methods based on the Convolutional Neural Network (CNN). Lin et al [10] proposed a CNN-based LEDNet network for light-emitting diode (LED) defect detection, and used Class Activation Mapping (CAM) [11] to achieve an automatic location of defects. Aiming at the problem of sample labeling difficulty for defect detection in actual industrial production, Lin et al [10] and Ren et al [18] used Class Activation Mapping (CAM), which is a class-discriminative localization technique that generates visual explanations from the CNN-based network to automatically locate surface defects.
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