An Improved Defect Detection Algorithm for Industrial Products via Lightweight Convolutional Neural Network

Abstract

Aiming at the problem that the existing computer vision detection algorithm based on deep learning consumes a lot of memory and computing resources, this paper improves the structure of convolutional neural network and proposes a lightweight algorithm for defect detection of industrial products by network pruning. The proposed algorithm uses the residual network to divide VGG-16 into different residual modules, introduces the sparse constraint of penalty factor and the attenuation constraint of weight matrix to measure the importance of each residual module, and cuts the residual modules with low importance, so as to greatly reduce the number of parameter learning in the deep residual network. Experiments show that this method can retain the accuracy, precision, recall and F1 score of the original network, and greatly improve the speed of network training to meet the real-time needs of product appearance defect detection.