An Enhanced YOLOv8-Based Approach for Foreign Object Detection on Transmission Lines

To overcome the limitations of existing transmission-line inspection models, including reduced detection precision in complex environments, inadequate performance for small objects and multi-scale targets, and high model complexity, a novel foreign object detection method for transmission lines is proposed in this study, based on an enhanced YOLOv8 architecture. First, the original YOLOv8 backbone is substituted with EfficientNetV2 to achieve model lightweighting while improving detection performance. Second, a Slim-neck module is integrated into the YOLOv8 neck to promote cross-layer information propagation and improve feature perception, which in turn boosts the detection performance on small objects. Meanwhile, an efficient multi-scale attention (EMA) is incorporated to boost multi-scale target detection performance, reduce computational overhead, and strengthen feature representation robustness. Finally, the localization performance of predicted targets is further improved by adopting MPDIoU rather than the original loss function. The experimental results indicate that the proposed method attains 97.7% precision, 95.6% recall, and a 97.5% mAP50, outperforming mainstream detection algorithms in comparative evaluations. Furthermore, relative to the baseline model, the Params and GFLOPs are reduced by 32.1% and 31.6%, respectively, thereby achieving a lightweight design and demonstrating its suitability for transmission-line foreign object detection.