CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection

In the printed circuit board (PCB) industry, cosmetic defect detection is an essential process to ensure product quality. However, existing PCB cosmetic defect detection approaches have a high false alarm rate, which lead to expensive labor costs of manual confirmation. To solve this problem, some traditional machine learning-based approaches have been proposed, but they just utilize hand-crafted features to build classifiers and thus are rough and sub-optimal. Recently, due to its powerful capability in automatic feature extraction, convolutional neural network (CNN) has been widely used in PCB cosmetic defect detection. However, few of them pay attention to the imbalanced class distribution as well as the different misclassification costs of real and pseudo defects, both of which are common problems in the PCB industry. To this end, in this study, we propose a novel model called cost-sensitive residual convolutional neural network (CS-ResNet) by adding a cost-sensitive adjustment layer in the standard ResNet. Specifically, we assign larger weights to minority real defects based on the class-imbalance degree and then optimize CS-ResNet by minimizing the weighted cross-entropy loss function. We conducted a series of experiments by comparing CS-ResNet with the standard ResNet, state-of-the-art CNN-based approach Auto-VRS and traditional machine learning-based approach HOG-SVM on a real-world PCB cosmetic defect dataset. Experimental results show that CS-ResNet achieves the highest Sensitivity (0.89), G-mean (0.91) and the lowest misclassification costs.