Crack identification of automobile steering knuckle fluorescent penetrant inspection based on deep convolutional generative adversarial networks data enhancement

As a key safety component of automobiles, automobile steering knuckles must be subjected to strict quality control. Currently, the identification of cracks in finished products primarily relies on manual identification of fluorescent penetrant detection. Owing to the complex shape of the workpiece, the interference of the displayed image and the small sample size, the accuracy of the automatic discrimination result of the fluorescent penetrant detection image is directly reduced. Therefore, this study proposed a data augmentation method based on deep convolutional generative adversarial networks (DCGAN) for crack identification in automotive steering knuckle fluorescent penetration inspection images. An image acquisition platform was built for fluorescence penetration detection of automobile steering knuckles, and fluorescence display images of various parts of the workpiece were collected. Based on the feature analysis of the displayed image, the image was preprocessed to suppress relevant interference and extract crack candidate regions. Further, using the original crack image to train DCGAN, several crack image samples were generated, the ResNet network was trained with the expanded dataset, and the extracted candidate regions were identified. Finally, the experimental results show that the recall rate of the crack recognition method used in this paper is 95.1%, and the accuracy rate is 90.8%, which can better identify the crack defects in the fluorescent penetrant inspection image, compared with the non-generative data enhancement method.