Enhancement multi-module network for few-shot leaky cable fixture detection in railway tunnel

Abstract

The acquisition process of leaky cable fixture in railway tunnel is very complicated, and the number of faulty samples that can be collected is very rare, which seriously restricts the detection accuracy of faulty fixture. Based on the few-shot learning algorithm, we propose the Enhancement Multi-module Network (EMN) to achieve leaky cable fixture detection and further improve the mean accuracy in this paper. Firstly, the multi-scale module and the multi-region module are presented to explore intra-class similarities and inter-class differences through different scale and region information. Then, we propose the fusion module to enhance feature expression, whose role is to distinguish the semantic information between fixture and background, thereby facilitating to identify fixture in highly similar tunnel scenes. Finally, the similarity scores between support and query fixture sample pairs are computed by the relation module, and the mean squared error loss is optimized by adding weight balance coefficient. Experiments are conducted on leaky cable fixture data set, the result demonstrates that our proposed algorithm outperforms excellent accuracy compared to several state-of-the-art methods based on traditional handcrafted feature algorithm, deep learning algorithm, and few-shot learning algorithm.