UAVs detect hazards with multi-directional Mamba on overhead transmission lines

Overhead transmission line hazard detection is related to the proper functioning of power communication systems and society. With the development of Unmanned Aerial Vehicles (UAVs) and deep learning, deep-learning-based hazard detection using UAVs has received extensive attention. Currently, research in this direction faces three main challenges: complex background interference, small-scale problems, and efficiency-performance balance. To address the above challenges, this study introduces Mamba based on State Space Models (SSMs) with linear complexity and proposes the UAV-MDMamba model for overhead transmission line hazard detection. We design a Multi-Directional Mamba (MDMamba) block to improve image spatial modeling and complex background suppression, which helps to capture hazardous areas in small-scale situations. Moreover, Patch-Level Inference Enhancement (PLIE) is designed to improve the detection accuracy of small targets in inference. Finally, we collect and label a dataset of overhead transmission line hazard detection for complex scenarios. Extensive experiments demonstrate that UAV-MDMamba performs excellently on the dataset. Therefore, this study improves the efficiency and accuracy of detecting hazards on overhead transmission lines.