# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 1 Introduction
[17] achieved excellent performance in insulator fault detection in aerial images with different backgrounds by improving the YOLOv3 model with a Spatial Pyramid Pooling (SPP) network and multi-scale prediction network, and the detection capability of the improved model in complex backgrounds was significantly enhanced. Extensive research on improving the performance of insulator defect detection has also been conducted [18; 19; 20; 21; 22]. The Single-Shot Detector (SSD) detection algorithm proposed by [PERSON] et al. [23] fuses the Bounding Box positioning method in the YOLO algorithm with the region-preselected-box method in the Faster Region-based Convolutional Neural Networks (R-CNN) [24] algorithm, so that the SSD can cope with images with low resolution when it can also guarantee a certain detection accuracy. Subsequently, the YOLO series algorithms [25; 26; 27] underwent multiple optimizations and improvements. Notably, the YOLOv3 algorithm was employed by [PERSON] et al. [28] for insulator positioning and fault detection tasks, demonstrating superior detection performance and speed. However, due to the high complexity of YOLOv3, including its large computational and parameter demands, its practical application in engineering projects remains limited. These studies have demonstrated the feasibility and effectiveness of fault detection and defect analysis of UAV inspection images based on deep learning techniques. However, there are still many issues that need to be further investigated, such as optimizing the performance of insulator defect feature detection for small targets, lightweighting the model to adapt to the hardware conditions of inspection UAVs, and reducing the number of network parameters so as to improve the real-time detection performance. To address the aforementioned issues, this paper conducts an in-depth study on intelligent detection and identification of insulator defects in transmission line inspections, which are together called the lightweight drone-based insulator defect detection method (LDIDD). The main contributions are as follows:For the problems of the small number of samples and unbalanced proportion in the existing dataset, this paper adopts a variety of data enhancement techniques, including methods such as brightness transformation and adding noise, to expand the size and diversity of the dataset. These enhancement techniques not only increase the number of samples in the dataset but also improve the robustness of the model. In addition, in this paper, the labels of discolored insulators are added on top of the existing labels of normal insulators and defective insulators. By incorporating the label type for discolored insulators, the dataset's label types are enriched, allowing the model to identify more types of insulator defects and enhancing the comprehensiveness and accuracy of the detection. 2. This paper takes the YOLOv8 target detection algorithm as the base network and makes lightweight improvement for its feature extraction module. Specifically, the MobileNetv3 model [29; 30; 31] is used to replace the C3 module and convolution module in the Backbone part of YOLOv8. MobileNetv3 adopts depthwise separable convolution and the BNeck module, which significantly reduce the computational complexity and parameter count of the model and make the model achieve lightweighting while maintaining high detection accuracy. In addition, this paper also optimizes the complexity of the improved model, which further improves the running efficiency of the model and makes it more suitable for deployment on resource-constrained embedded systems and mobile devices. 3. Recognizing that insulator defects are often small target features in captured images susceptible to misidentification or oversight, this study integrates a lightweight attention mechanism to alleviate these challenges.