A deep learning-based approach for the automated surface inspection of copper clad laminate images

Surface quality inspection and control are extremely important for electronic manufacturing. The use of machine vision technology to automatically detect the defects of products has become an indispensable means for better quality control. A machine vision-based surface quality inspection system is usually composed of two processes: image acquisition and automatic defect detection. In this paper, we propose a deep learning-based approach for the defect detection of Copper Clad Laminate (CCL) images acquired from an industrial CCL production line. In the proposed approach, a new convolutional neural network (CNN) that realizes fast defect detection while maintaining high accuracy is designed. Our proposed approach makes four contributions. First, we introduce the depthwise separable convolution to reduce the calculation time. Second, we improve the squeeze-and-excitation block to improve network performance. Third, we introduce the squeeze-and-expand mechanism to reduce the computation cost. Fourth, we employ a smoother activation function (Mish) to allow improved information flow. The proposed network is compared with the benchmark CNNs (including Inception, ResNet and MobileNet). The experimental results show that compared with the benchmark networks, our proposed network has achieved the best results regarding the accuracy and suboptimal results in terms of the speed compared with the benchmark networks. Therefore, our proposed method has been integrated into an industrial CCL production line as a guideline for online defective product rejection.