# Insulators and Defect Detection Based on the Improved Focal Loss Function
## 1 Introduction  
During the routine inspection of transmission lines, the main methods include manual inspection, helicopter inspection, robot inspection, and unmanned aerial vehicle (UAV) inspection [1]. With the development of technology, UAVs have gradually become an important method for transmission line detection because of their low cost, convenient operation, and high efficiency. Power supply companies take a large number of inspection pictures during daily inspections through drone inspections. However, these pictures currently mainly rely on manual recognition, which is less efficient and accurate. Therefore, using deep learning technology to detect insulator defects quickly and accurately is the new direction of power inspection development [2]. In recent years, with the development of deep learning theory and the improvement of computer hardware performance, the research on transmission line defect detection based on deep learning has become a hot topic. At present, there are two main kinds of neural networks commonly used in target detection. The first is a two-stage target detection model based on region extraction, such as faster region-based convolutional neural networks (Faster R-CNN) [3], and the second type is based on regression single-stage target detection, such as \"You Only Look Once\" (YOLO) [4], Single Shot MultiBox Detector (SSD) [5], etc. These two models are widely used in current research. For example, [PERSON] et al. [6] proposed a new method for assessing the condition of transmission overhead lines, which can automatically isolate transmission poles, disaggregates components, detects defects, and determines the health index of concrete structures and insulators, compared to traditional foot patrol visual inspections. This approach greatly improves efficiency and reduces costs. [PERSON] et al. [7] have developed a model for automatically estimating and locating thedamage process of distribution rods, which uses images taken by drones to be transmitted in real time to the Intelligent Damage Classification and Estimation (IDCE) unit. The model has high accuracy in the classification and estimation of the damage of distribution poles. [PERSON] et al. [8] proposed a method of fault detection and evaluation of transmission lines based on deep learning is presented. It can effectively identify eight transmission line component defects, such as insulator defects, three-plate defects, damper defects, graded ring defects, etc., as well as nesting and foreign objects attached to the transmission line. [PERSON] et al. [9] proposed an SSD algorithm based on candidate regions to locate and identify insulator and anti-vibration hammer defects. [PERSON] et al. [10] constructed an automatic defect detection model called Automatic Visual Shape Clustering Network (AVSCNet) for the detection of missing parts of bolts, and its detection accuracy can reach up to 87.6%. [PERSON] et al. [11] proposed a method for detecting insulator defects based on an improved YOLOv4 algorithm. Graph Cut-based image enhancement and Laplace sharpness were used to preprocess insulator images, and YOLOv4 model architecture was improved using Mobile Net lightweight convolution neural network. Combined with the migration learning method, the detection accuracy can be improved to 97.26% after image preprocessing. [PERSON] et al. [12] proposed an optical image detection method based on deep learning and morphological detection. Faster RCNN is used to locate insulators and extract their target image from the detection image. A mathematical model is built to describe the defect in the binary image to improve the precise location of the defect. The detection effect of the method based on deep learning is greatly limited by the amount and distribution of data. Unbalanced data samples will have a greater impact on the detection results. The number of intact insulators in transmission line defect detection is much larger than the number of insulators with self-burst defects.