Insulator Defect Detection Technology Based on Deep Learning

With the development of power system, the number and scale of power equipment continue to expand, and the risk of equipment failure is also increasing. Therefore, timely and accurate fault detection becomes particularly important. As a key part of high-voltage transmission lines, insulators are exposed to complex and variable outdoor environments for a long time, and are susceptible to faults or defects due to various factors. In the traditional power grid system, the maintenance of high-voltage transmission lines usually relies on manual patrol, which has security risks and high costs. However, the development of UAV technology and image recognition technology provides a better solution for insulator defect detection. Compared with the traditional manual patrol, the UAV patrol strategy significantly improves the detection efficiency and security, and even can realize remote monitoring and real-time early warning of the power grid system. Based on YOLOv8 target detection algorithm, this paper conducts an in-depth study on insulator defect detection technology. In order to enhance the recognition ability of the model for important features, this paper adds the CBAM attention mechanism to the YOLOv8 algorithm. This mechanism combines spatial attention and channel attention, so that the model can capture and focus on important features more accurately, thereby improving the detection accuracy of insulator defects. At the same time, in order to solve the problem of incomplete identification of small targets in defect detection, this paper adds a small target detection head to improve the detection ability of the model for small targets and ensure the comprehensiveness and accuracy of insulator defect detection. Finally, ablation experiments verify the effectiveness of the proposed method in detecting insulator defects, and a series of comparative experiments are carried out with several mainstream target detection algorithms. The experimental results show that the improved model effectively improves the detection accuracy while ensuring the operating efficiency. This result not only verifies the efficiency of the improved method in this paper, but also provides technical support and theoretical reference for future smart grid systems.