FDML: An Improved Few-Shot Fault Detection Method for Transmission Lines Based on Meta-Learning

In the task of fault detection in power transmission lines, certain fault categories suffer from the problem of insufficient samples, leading to inefficiencies in traditional object detection algorithms. Meta-learning, which employs multi-task learning and fine-tuning to extract common features across different tasks, performs well in few-shot object detection and demonstrates excellent generalization capabilities for new tasks. For this reason, an improved few-shot fault detection method based on meta-learning FDML is proposed in this paper. Firstly, to address the problem of distribution difference in data domains, a two-stage meta-learning training method is introduced to achieve model migration through meta fine-tuning. Secondly, we propose a support and query feature matching module SQFM to make the utmost of support features to assist detection, in which prototype features of the support class are accurately extracted in the first three stages of the backbone and then assigned to the query set features to highlight the class-specific representative features. To further integrate high-level feature before model prediction, a high-level semantic feature fusion module HSFF is designed to fuse RoI features and prototype features via combining the four feature fusion ways. Experimental results show that FDML effectively improves the few-shot object detection accuracy on the public dataset PASCALVOC and the fault dataset InsPLAD-fault, compared to the classic few-shot algorithms. Under the conditions of K= {5, 10, 20} shot in the fault dataset InsPLAD-fault, the mAP50 values are respectively 5.7 , 7.2 and 4.2 higher than the baseline network, which provides a solution for few-shot transmission line fault detection.