An edge-enhanced foreign object detection approach for transmission lines in complex environments

Abstract

Transmission lines are typically exposed to open environments for extended periods, rendering them susceptible to foreign object intrusions that can lead to power system failures. Conventional detection methods rarely account for complex weather conditions. To address the issue of low detection accuracy under such scenarios, this study proposes an improved foreign object detection algorithm, termed EE-DETR, based on RT-DETR. Specifically, a learnable edge enhancement module is designed to mitigate the challenges posed by weak textural features and low contrast between floating foreign objects and the background. Additionally, a dual-branch attention structure is introduced into the encoder, enabling the model to effectively capture high-frequency local details while preserving global low-frequency context in complex environments involving rain, snow, and fog. Experimental results demonstrate that, in contrast to the baseline, our approach improves precision, recall, and mAP50 by 3.7%, 4.2%, and 2.8%, respectively.