A Lightweight YOLOv4 EDAM Model for Accurate and Real time Detection of Foreign Objects Suspended on

Foreign objects suspended on transmission and distribution lines like nests, kites, balloons, and trash, etc. may lead discharge faults and greatly affect the safety of power grid. How to perform accurate and timely detection of these foreign objects is an urgent problem. This paper proposes a lightweight YOLOv4 model with embedded dual attention mechanism (YOLOv4-EDAM) to detect foreign objects from visible images, using MobileNetV2 embedded with the squeeze and excitation networks (SENet) to replace the CSPDarkNet53 feature extraction network, the depthwise separable convolutions (DSC) to replace the standard convolutions in SPP and PANet module, and embedding the convolutional block attention module (CBAM) into SPP and PANet module to improve the detection accuracy. Firstly, an image dataset was constructed using inspection images and public datasets, and expanded by Poisson blending and some data augmentation methods. The denoising convolutional neural network (DnCNN) was applied for image preprocessing. Next, the lightweight YOLOv4-EDAM model was trained combining Mosaic data enhancement, cosine annealing and label smoothing skills. Several detection cases were carried out and the experimental results show that the proposed model has a high accuracy with the mean average precision (mAP) of 96.71%, and a fast detection speed with the frames per second (FPS) of 45, whose overall performance is better than other object detection models. This study offers a reference for power line inspection and provides a possible way to deploy edge computing devices on unmanned aerial vehicles.