Research for Defect Detection based on Few-Shot Learning of Siamese Network

Abstract

Aiming at the problems of inefficient detection caused by traditional manual inspection, unclear features and few defect sample in metal surface defect detection, an improved metal surface defect detection technology the siamese network is proposed. The proposed method constructs a deep siamese network with maximum interval, which consists of 4 convolutional layers, 4 max pooling layers and the top fully connected layer to improve the accuracy of the recognition of difficult small targets while ensuring the detection efficiency of the model. The performance of the improved metal surface defect detection technology is compared with other detection methods on the NEU surface defect database. The experimental result verify that the mean average precision of the proposed method is 96.1%, which is higher that of support vector machine, region convolutional neural network, faster region convolutional neural network, single shot multibox detector, you only look once neural network v3.