Real-time defect detection network for polarizer based on deep learning

Quality analysis of the polarizer of a production line can be performed using image processing technology. The existing method of detecting defective images based on deep learning can ensure accurate classification; however, its detection speed is low, the model requires a large amount of memory, and it is difficult to meet the real-time requirements of online detection systems when hardware resources are limited. Therefore, in this study a lightweight polarizer defect detection network, called DDN, was developed based on deep learning. First, a parallel module was designed to build the network. This module has two main advantages. First, it mixes different convolution template sizes, and can fuse the features of different scales and extract more defect features than the traditional convolution layer. Second, depthwise separable convolution is used to replace full convolution in this module, which significantly reduces the number of parameters and the multiply-accumulate operations. Finally, a global average pooling (GAP) layer is used instead of a fully connected layer. The GAP layer has no parameters to optimize, which substantially reduces the number of network parameters. Experimental results show that the proposed method is better than existing methods in terms of classification speed, precision, and memory consumption for polarizer detection, and can satisfy real-time requirements.