Lightweight Abnormal Object Detection in Transmission Line Corridors Considering Long-Tail Distribution

Abstract Due to the limited capacity to capture low-probability features of abnormal objects in long-tail distributions, intelligent and efficient abnormal object detection still fails to achieve ideal performance in practical environments. Therefore, a lightweight method for abnormal object detection in transmission line corridors is proposed to address the inter-class distribution differences and intra-class attribute imbalance exhibited by abnormal objects. It integrates a class inherent feature extraction module (CIFEM) and a self-moving channel attention module (SMCAM), called LAOD-LTNet. Firstly, the CIFEM is proposed to capture intrinsic features, enhance the understanding of intra-class variations and inter-class similarities, and reduce the negative impacts on tail classes. Then, the feature representation is enhanced using the proposed SMCAM to balance the compactness and accuracy of the model. Finally, the experimental results on the transmission line corridor abnormal object dataset (TLCAOD) show that the proposed method can effectively maintain the coordination between lightweight architecture and high accuracy, providing innovative ideas for overcoming long tail data on transmission line corridors.