Surface defects inspection of cylindrical metal workpieces based on weakly supervised learning

Weakly supervised learning applies image tag labels to train convolutional neural networks to locate defect. In industrial vision system, metal surface is anisotropic under light in all directions and it is inevitable to cause local overexposure due to the natural reflection of active strong light, especially on the cylindrical metal surface. In this paper, injector valve is taken as the representative of cylindrical metal workpieces. Since the variety and complexity of cylindrical metal workpiece defects which cause pixel-level annotation require expensive manual work. This problem hinders the application of convolutional neural network in industries. In order to solve these above challenges, this paper proposed an end-to-end weakly supervised learning framework named Integrated Residual Attention Convolutional Neural Network (IRA-CNN). IRA-CNN only uses image tag annotation for training and performs defect classification and defect segmentation simultaneously. Weakly supervised learning is achieved by extracting category-related spatial features from defect classification scores. IRA-CNN is composed of multiple Integrated Residual Attention Block (IRA-Block) as the feature extractor which improves the accuracy and achieves real-time performance. IRA-Block adds Integrated Attention Module (IAM) which includes channel attention submodule and spatial attention submodule. The channel attention submodule adaptively extracts the channel attention feature map to improve its bilateral nonlinearity and the robustness. IAM can be well integrated into the IRA-CNN makes the neural network suppress the interference of useless background area and highlight the defect area. Satisfied performance is achieved by the proposed method in our own defect dataset which could meet the requirements in the industrial process. Experimental results show that the method has good generalization ability. The accuracy of defect classification reaches 97.84% and the segmentation accuracy is significantly improved compared with the benchmark method.