Center-Triplet Loss for Railway Defective Fastener Detection

Abstract

Sample imbalance is the main factor affecting detection performance of defective fasteners. Most of the existing methods focus on leveraging image generation and unsupervised idea to overcome sample imbalance and realize detection of defective fasteners, while learning discriminative features with deep metric learning for imbalanced fastener detection is neglected. Thus, we study the variants of deep metric learning losses and propose a novel loss named center-triplet loss for defective fastener detection in the case of imbalanced samples. Specially, the proposed center-triplet loss can learn a center for each fastener state and this center is closer to the same fastener state than to different fastener states. In this way, the learned features are more robust and discriminative and it is beneficial to realize accurate detection of defective fasteners when samples are uneven. Extensive experiments are conducted on the constructed fastener dataset, and the results demonstrate the effectiveness and superiority of our proposed loss for the detection of imbalanced fasteners.