Search

Suspended foreign objects on transmission lines will shorten the discharge distance, easily leading to phase-to-ground or phase-to-phase short circuits, which induces outage accidents. Foreign objects are small and difficult to identify, resulting in low detection accuracy. An improved foreign object detection method based on Swin Transformer V2 and YOLOX (ST2Rep–YOLOX) is proposed. First, the feature extraction layer ST2CSP constructed by Swin Transformer V2 is used in the original backbone network to extract global and local features. Secondly, hybrid spatial pyramid pooling (HSPP) is designed to enlarge the receptive field and retain more feature information. Then, Re-param VGG block (RepVGGBlock) is introduced to replace all 3 × 3 convolutions in the network to deepen the network and improve feature extraction capabilities. Finally, experiments are carried out on the transmission lines foreign object image dataset, which was obtained using unmanned aerial vehicles (UAVs). The experimental results show that the average accuracy of the ST2Rep–YOLOX method can reach 96.7%, which is 4.4% higher than that of YOLOX. The accuracy of the nest, kite, and balloon increased by 9.3%, 15.4%, and 9.6%, and the recall increased by 6.5%, 9.4%, and 2.5%, respectively. This method has high detection accuracy, which provides an important reference for foreign object detection in transmission lines. [ABSTRACT FROM AUTHOR]