# Research on an Insulator Defect Detection Method Based on Improved YOLOv5
## 1 Introduction  
Transmission lines often need to cross mountains and rivers and are primarily distributed in complex terrains such as mountains and hills and harsh climate environments, significantly complicating line inspections. Insulators have been operating in harsh and complex environments such as strong electric fields, high-temperature sunlight, and mechanical stress for a long time. When their degradation reaches a certain level, their insulation performance will decrease [1, 2, 3, 4]. Especially on high-voltage transmission lines, the deterioration of insulators directly threatens the safe operation of power systems. In order to ensure the stable and safe operation of the power grid, the defect detection of power insulators has become an essential task in the surveillance of power systems. Therefore, it is significant to study insulator image recognition and defect detection methods [5]. In recent years, with the maturity of deep learning and the limitation of image recognition technology, an earlier insulator defect detection method used the edge recognition feature method to extract the insulator's shape. Then it used the elliptic equation to fit the contour and finally determined the missing parts of the insulator using the analysis and counting method. There is also a method of using the image filtering algorithm to intercept the insulator's edge to realize insulator image recognition. The above method could be more computationally complex and efficient. With the development of artificial intelligence technology, computer vision has been gradually applied to detect defects in transmission line insulators. The current deep learning-based target detection recognition algorithms are broadly classified into two categories. One class is the target detection algorithm based on region suggestion, such as the representative algorithms: recurrent neural networks (R-CNN) [6], Fast R-CNN [7], Faster R-CNN [8], spatial pyramid pooling networks (SPP-Net) [9], and so on. Another category is regression-based target detection and recognition algorithms such as the single shot multibox detector (SSD) [10], and the YOLOv2 [11], YOLOv3 [12], YOLOv4 [13], etc., in the you-only-look-once (YOLO) [14] series. [PERSON] et al. [15] proposed a transmission line ice thickness identification method based on a combination of a MobileNet v3 lightweight feature extraction network and an SSD detection network, with an accuracy of 74.5%. [PERSON] et al. [16] constructed an automatic defect detection model called the automatic visual shape clustering network (AVSCNet) to detect missing parts of bolts, and its detection accuracy can reach up to 87.6%. [PERSON] et al. [17] used Faster RCNN to detect defects on distribution lines in each frame of UV-visible video, then identified corona discharges on the lines by color thresholding, and finally described the severity of the occurrence of faults by the ratio of the area of spots to the area of defects. [PERSON] et al. [18] applied Faster RCNN, Hough transform, and advanced stereovision (SV) to detect vegetation encroachment on power transmission lines, converting two-dimensional (2D) images of vegetation and power transmission lines into three-dimensional (3D) height and location results to obtain accurate identification and localization. [PERSON] et al. [19] proposed a YOLOv5 target detection model based on the method for the automatic detection of insulator defects. Compared with four different versions of the YOLOv5 model, the YOLOv5x model based on k-mean clustering can effectively identify and locate insulator defects in transmission lines. However, the maximum accuracy is only 86.8%. [PERSON] et al. [20] proposed the MTI-YOLO network model, which uses a multi-scale detection head and multi-scale feature fusion structure to improve the model's detection accuracy, but only detects defects of common insulators. [PERSON] et al.