Review on an Insulator Fault Detection Using Improved Yolo V5

Insulators are critical components of high-voltage power systems, guaranteeing efficient electrical transmission by reducing leakage currents and energy loss. However, a variety of environmental and operational variables can cause insulator deterioration, resulting in cracks, punctures, or contamination. These flaws can have a substantial influence on power transmission dependability, potentially resulting in disruptive outages, safety risks, and higher maintenance costs. Traditional problem detection technologies, such as physical inspection and infrared imaging, are sometimes time-consuming, labor-intensive, and subject to human error. In this research, we provide a deep learning-based automated fault detection system that uses the You Only Look Once (YOLO) method for real-time object identification and classification of insulator flaws. YOLO is a cutting-edge convolution neural network (CNN) model built for quick and efficient detection, capable of simultaneous localization. YOLO is a cutting-edge convolution neural network (CNN) model that can detect and localize many types of insulator flaws in a single picture. Using a large-scale dataset of insulator photos tagged with defect categories, the YOLO-based model is trained to identify between healthy and defective insulators with high precision.