Defect Detection Method of Aluminum Profile Surface Using Deep Self-Attention Mechanism Under Hybrid Noise Conditions

Noisy images will inevitably exist in the image acquisition process of the aluminum profile surface (APS) due to the limitations of the imaging system. And, the overlap of different types of image noise will increase the difficulty of image-based detection for APS. Addressing this problem, we propose an APS defect detection method based on a deep self-attention mechanism (DSAM) under hybrid noise conditions. Firstly, we use the residual learning strategy to obtain defect feature maps from noisy images through identity mapping. This strategy overcomes the limitations of the shallow convolutional neural network (CNN) on feature extraction. Then, the self-attention mechanism achieves refined feature extraction by adding corresponding weight matrices to the defect feature maps. The mechanism improves the defect feature extraction ability under hybrid noise conditions. Finally, a softmax classification layer is added to the feature output layer to establish the mapping relationship between the feature space and the APS defect space. And the APS defect detection result under hybrid noise conditions is obtained. Experimental results demonstrated that the proposed method can achieve higher recognition accuracy under hybrid noise conditions.