# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 1 Introduction  
Insulators [1, 2, 3, 4] are one of the most common electrical components in transmission and distribution lines, and because insulators are exposed to the natural environment for a long time, they are susceptible to defects or self-detonation under the influence of extreme weather such as gusty winds and heavy rains, which affect the normal operation of transmission and distribution lines; therefore, the state of insulators is an important element of electric power inspection. The emerging Unmanned Aerial Vehicle (UAV) inspection technology, compared with the traditional manual inspection method, presents advantages of high flexibility, high security, high economy, etc. It can effectively solve problems of high danger levels in manual power-carrying operations, high labor intensity due to the wide distribution of lines, etc. [5, 6, 7, 8], which are of great significance for ensuring the safety of electric power. Although the use of drones for photo shooting and analysis is more efficient than traditional manual on-site inspections, the images obtained by drones usually have a wide field of view, resulting in a complex and cluttered background of the insulator. In addition, a single image may contain multiple insulators at the same time, or the insulator target may be small. In this context, the detection of insulator defects faces significant challenges. Therefore, there is an urgent need for a detection method for small target insulators and their defects in complex backgrounds to improve detection efficiency. In recent years, there have been many studies on collecting images with drones and then analyzing and detecting insulator defects with the help of deep learning algorithms. [PERSON] et al. [9] introduced prior knowledge on insulators, namely, the edge information of insulators, to improve the accuracy of model detection. Their experimental results show that the improved ResNet-18 model can detect insulator self-explosion defects with an accuracy of 95.1%, and the method can be applied to different types of CNN networks. [PERSON] et al. [10] proposed combining the insulator multi-fault target detection algorithm with deep learning to automatically learn the high-level features of multiple insulator images with different surface faults and added traditional low-level visual features (color features and texture features) to more fully extract the effective features of the image, thereby improving the recognition accuracy. The insulator fault detection method proposed by [PERSON] et al. [11] includes two steps. First, a new neural network is designed to accurately obtain the position of the insulator. Then, an insulator fault location method based on RoI (Region of Interest) is designed. The experimental results on various aerial images of insulators show that this method has high average location accuracy and low running time. [PERSON] et al. [12] proposed a defect detection method based on incremental learning for the problems of slow detection speed and high false detection rate in existing defect detection methods. This method can quickly screen and locate defective bolts in high-resolution images, thus assisting inspectors to a certain extent and improving work efficiency. [PERSON] et al. [13] proposed a more effective detection method based on the YOLOv7 [14] model by introducing the Kmeans++ algorithm and an attention mechanism. This method is able to achieve the detection of tiny insulator targets in complex backgrounds, which substantially improves the accuracy of detection. [PERSON] et al. [15] added a Coordinate Attention (CA) mechanism to YOLOv5 [16], proposed to replace the C3 network with a Transformer encoder block, and used a small target orientated detection head in order to improve accuracy in detecting small target insulators in UAV images, which significantly enhanced the detection effect. [PERSON] et al.