A Lightweight Transmission Line Foreign Object Detection Algorithm Incorporating Adaptive Weight Pooling

Aerial photography using unmanned aerial vehicles (UAVs) to detect foreign objects is an important method to ensure the safety of transmission lines. However, existing detection algorithms often encounter challenges in complex environments, including limited recognition capability and high computational demands. To address these issues, this paper proposes YOLO-LAF, a lightweight foreign object detection algorithm that is based on YOLOv8n and incorporates an innovative adaptive weight pooling technique. The proposed method introduces a novel adaptive weight pooling module within the backbone network to enhance feature extraction for detecting foreign objects on transmission lines. Additionally, a multi-scale detection strategy is designed to integrate the FasterBlock and EMA modules. This combination enables the model to effectively capture both global and local image features through cross-channel interactions, thereby reducing misdetection and omission rates. Furthermore, a C2f-SCConv module is introduced in the neck network to streamline the model by eliminating redundant features, thus improving computational efficiency. Experimental results demonstrate that YOLO-LAF achieves average accuracies of 91.2% and 85.3% on the Southern Power Grid and RailFOD23 datasets, respectively, outperforming the original YOLOv8n algorithm by 2.6% and 1.8%. Moreover, YOLO-LAF reduces the number of parameters by 23.5% and 14.8% and the computational costs by 19.9% and 24.8%, respectively. These improvements demonstrate the superior detection performance of YOLO-LAF compared to other mainstream detection algorithms.