# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 1 Introduction  
As the basic component of transmission lines, suspension insulators not only have to withstand the vertical and horizontal loads and the tension of conductors, but also withstand the working voltage and over voltage, so they must have good electrical insulation and mechanical properties. In practical engineering, insulators usually operate in complex and diverse natural environment for a long time, which may lead to failures or defects such as self-shattering, string breakage, crack, and foreign body erosion, etc. [1, 2]. The insulation degradation of defective insulators will seriously affect the safety of transmission lines. Therefore, accurate and rapid detection of insulator defects is of great significance to avoid large-scale power outages and reduce power grid losses. Transmission line inspection methods mainly include manual inspection, helicopter inspection [3], robot inspection [4], and unmanned aerial vehicle (UAV) inspection [5]. Manual inspection method is not easy to find defects on the line under special geographical environment and meteorological conditions, and the inspection efficiency is low. Helicopter inspections are usually with high investment costs, high safety risks, and high technical requirements. UAV has gradually become an important means of transmission line inspection by virtue of its low cost, good operability and flexibility, and high efficiency [6]. UAV inspections will generate a large amount of image information, and the manual judgment by inspectors is inefficient and easy to cause false recognition, while deep learning technology can meet the needs of intelligent processing and analysis of massive inspectionimages, which is an effective method to achieve automatic identification of transmission line defects. At present, deep learning has been widely applied for inspection image recognition of transmission lines. For example, [PERSON] et al. [7] proposed a method to identify ice thickness of transmission lines based on MobileNetV3 feature extraction network and SSD detection network, which can reach an accuracy of 74.5%. [PERSON] et al. [8] used region of interest (ROI) mining, K-means clustering algorithm, and focal loss function to improve the Faster RCNN detection network, so as to achieve accurate detection of bird's nests on transmission lines. [PERSON] et al. [9] proposed an automatic visual shape clustering network (AVSCNet) for transmission line pin-missing defect detection, and its mean average precision (mAP) for bolts location and pin-missing defect detection can reach 0.724. [PERSON] et al. [10] applied the SPTL-Net shared convolutional neural network (CNN) to improve Faster RCNN, so as to achieve accurate detection of foreign objects on transmission lines. [PERSON] et al. [11] proposed a method of damage detection and assessment of transmission towers based on ResNet convolutional neural network. [PERSON] et al. [12] used Faster RCNN to detect the equipment on power distribution lines in each frame of the UV-visible video, and then identified the corona discharge of the line by the color threshold, and finally described the severity of the fault by the ratio of the spot area to that of the equipment. [PERSON] et al. [13] applied Faster RCNN, Hough transform, and advanced stereovision (SV) to detect vegetation encroachment of power lines, and convert the two-dimensional (2D) images of vegetation and power lines into three-dimensional (3D) height and location results to obtain precise geographic location. The deep learning algorithms have also been widely applied for identification and location of transmission line insulators and their defects. For example, [PERSON] et al. [14] designed a new type of cascaded CNN and used image augmentation method to increase the number of defective insulator images. The precision and recall rate of insulator defect detection can reach 91% and 96%, respectively. [PERSON] et al.