WCANet: An Efficient and Lightweight Weight Coordinated Adaptive Detection Network for UAV Inspection of Transmission Line Accessories

Abstract

Accurate detection and timely management of high-voltage transmission accessories are crucial for ensuring the safe operation of power transmission. Existing network models suffer from issues like low precision in accessory detection, elevated model complexity, and a narrow range of category detection, especially in UAV-based inspection scenarios. To alleviate the above problems, we propose an innovative Weight Coordinated Adaptive Network (WCANet) in this paper, aiming to improve the efficiency and accuracy of high-voltage transmission accessories detection. The network is designed with a plug-and-play WCA module that can effectively identify dense small targets, retain information in each channel, and reduce computational overheads, while incorporating Sim-AFPN with a skip-connection structure into the network aggregate feature information layer by layer, enhancing the ability to capture key features, and achieving a lightweight network structure. The WIoU loss of bounding box regression (BBR) is to reduce the competitiveness of high-quality anchor boxes and mask the effects of the low-quality examples, thus improving the accuracy of the model. The experimental results show that WCANet has achieved remarkable results in the HVTA, VisDrone2019, and VOC2007 datasets. Compared with other methods, our WCANet achieves highly accurate prediction of high-voltage transmission accessories with fewer parameters and model sizes, availably balancing model performance and complexity.

Keywords

• transmission line
• object detection
• attention module
• convolutional neural network
• feature pyramid network