# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
###### Abstract  
As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection. insulator detection; YOLOv5; small target detection; multi-scale edge  
the power system poses a serious threat, ref. [3] and the insulator's working condition directly affects the safety and stability of the power grid [4]. The traditional insulator inspection method is mostly manual inspection, which is labor-intensive and has low safety [5]. The detection process is susceptible to environmental and human factors. Based on many factors, Unmanned aerial vehicles (UAVs)are now used to detect insulators and other electrical equipment in transmission lines [6]. The main methods commonly used for insulator detection are artificial-based detection methods and machine learning-based detection methods. In the artificial detection algorithm, [PERSON] [7] used ultrasonic technology to detect the density of basin insulators. [PERSON] et al. [8]. proposed a new data augmentation method to reduce the adverse effect of unbalanced dataset distribution on detection performance during network model training, and optimized the parameters of the support vector machine by genetic algorithm. The authors of [9; 10] extracted feature information for insulator detection based on different color models. [PERSON] et al. [11] combined the spatial characteristics and color characteristics of insulators to segment the insulators and background in the image and used the morphological algorithm [12] to locate the insulator defect area. The authors of [13; 14; 15] detected insulators according to their texture and shape characteristics. [PERSON] et al. [16]. extracted the shape and texture information of the insulator as a priori conditions, combined with the active contour model, to realize the segmentation of the insulator in a complex background. In the artificial detection algorithm, the color and shape of the image will change due to the influence of illumination, shooting distance, and angle when the UAV captures the image. The detection results of this method are susceptible to interference from the surrounding environment, and the experience of inspectors will also affect the detection accuracy is limited by specific conditions. In the algorithm based on machine learning, [PERSON] et al. [17] based on the mask region convolutional neural network (R-CNN) model, changed the network structure according to the size of the detected target insulator and used a genetic algorithm to optimize the hyperparameters in the network. Based on fast R-CNN [18], [PERSON] et al. [19] replaced the original Visual Geometry Group16 (VGG16) with a more complex feature extraction network to improve the ability of the network model to obtain image feature information and reduce information redundancy by adding an attention module. In 2016, [PERSON] et al. [19] first proposed a single-stage object detection algorithm, which pioneered the You Only Look Once (YOLO) [20; 21; 22; 23] series.