YOLO-AFPN: Marrying YOLO and AFPN for external damage detection of transmission lines

Abstract

To better detect targets that may cause external damage to transmission lines, the authors present You Only Look Once-Asymptotic Feature Pyramid Network (YOLO-AFPN), a lightweight but efficient model. Firstly, the authors adopt a feature comparison strategy based on the knowledge of transmission line scenes, which facilitates increased attention to target features during the training. Secondly, the YOLOv8 detection network is built, and the backbone adds three layers of simple parameter-free attention module, which extracts features while maintaining lightness, and improves the detection capability in complex scenarios. Then, in the feature fusion stage, an AFPN is constructed, which improves the multi-scale target detection capability while reducing the number of model parameters by asymptotically fusing features that have small semantic gaps between neighbouring layers. When during the training process, the improved Mosaic data augmentation method is used to enhance the number of distributions of small targets, improve the robustness of the model. Finally, the improved model is validated, and the experimental results show that the improved model can achieve mean average precision of 86.1% at 6.6 MB, which is better than the original network for detection and meets the requirements for deployment on edge devices.