GFENet: a lightweight network for efficient detection of abnormal objects in transmission line corridors

Abstract

Abstract Abnormal objects in transmission line corridors pose a grave hazard to the security of the power system. The intelligent edge monitoring system, driven by a lightweight model, offers a promising approach. However, due to the limitations of the lightweight networks in addressing various uncertainties of anomalous objects, effectively balancing the efficiency and accuracy of existing methods presents a significant challenge. Therefore, a lightweight network called GFENet is proposed, which is designed to effectively address missed and false detections from the fluctuation in similarity of inter-class features and operational characteristics, as well as the diversity in intra-class shapes and scales under complex conditions. Firstly, learnable and efficient channel attention is proposed. This mechanism utilizes hybrid pooling fusion and weighted learning adjustment strategy to expand the receptive field, thereby capturing the distinctive visual features of the object. Next, we introduce feature pyramid network and path aggregation network to facilitate multi-scale feature interactions. Then, an efficient dynamic head is proposed, which employs a keypoint offset strategy to achieve scale, spatial, and task awareness. This enhances the understanding of object structure and shape without increasing computational costs. Finally, the experimental results on self-built dataset demonstrate that the GFENet can virtually balance network lightweighting and accuracy, significantly enhancing the ability to detect foreign object intrusions in complex environments.