LA-YOLO: Bidirectional Adaptive Feature Fusion Approach for Small Object Detection of Insulator Self-Explosion Defects

The unmanned aerial vehicle (UAV) inspection technology based on the deep learning has been widely used in the detection of insulator self-explosion defects (ISDs) for power systems. Although existing methods have achieved the high average precision (AP) in general scenarios, they perform poorly in small object scenarios captured by UAVs and lack generalization. Moreover, most of these methods rely on model structures with large parameters and high floating-point operations (FLOPs), and can only be deployed on cloud computing servers. In response to the above issues, this paper proposes a lightweight adaptive you only look once (LA-YOLO) approach of small object detection for ISDs. Firstly, the Faster-C2f module based on the partial convolution (PConv) is introduced into the backbone network to reduce the parameters and FLOPs of redundant channels during gradient diversion. Secondly, the bidirectional adaptively feature pyramid network (Bi-AFPN) dedicated to small object detection layer adaptively learns the weights of feature fusion at different scales from both spatial and channel dimensions. This method improves the effectiveness of the feature information for small-scale ISDs in the process of feature fusion at different scales. Finally, the task and structure dual decoupling head (DD-Head) introduces the spatial aware convolution (SAConv) in the regression branch to extract spatial feature information in both horizontal and vertical directions. The redundant convolution calculations in the classification branch are also reduced, significantly. The experimental results demonstrate that the LA-YOLO not only has lower parameters and FLOPs, but also outperforms existing methods (InsuDet, ID-YOLO, FINet, and BiFusion-YOLOv3) in the average accuracy of small object detection scenarios in ISDs. Our approach is easier to deploy on the UAV and has good application prospects.