Fault Diagnosis of Transmission Lines Based on Sketch Retrieval for Small Targets and Small Samples

Transmission lines (TLs) are the backbone of the power system, enabling efficient and reliable transmission of power over long distances. Fault diagnosis is the key technology of maintenance. Currently, there are several computer vision-based fault detection methods for power equipment. However, valuable fault samples from complex environments are difficult to collect, and many device faults exist in the form of small targets. Inspired by sketch-based retrieval, we first propose a method for TL fault diagnosis (TLFD) based on hand-drawn sketches. First, to address the problem of incomplete feature extraction, a densely connected multiscale feature network is proposed. Second, to address the problem of insufficient use of prior knowledge, a domain-aware dual attention network is proposed. Finally, a similarity loss function is constructed to supervise the similarity between all images, which preserves the prior knowledge in the pretrained model. To study the instance-level relationship between hand-drawn sketches and real images, we created the “TLFD” data. Experiments demonstrate that we provide a new idea for power system fault diagnosis, and the model significantly outperforms many state-of-the-art models designed for fault diagnosis.