Exploring Power Line Arc Detection Using YOLOv8 Computer Vision Models

The power grid stands as a cornerstone of modern living, facilitating infrastructure, industry, and daily conveniences through a reliable supply of electricity. However, the transmission of power is beset by challenges such as maintenance costs, malfunctions, and losses due to resistance. Of particular concern in high voltage transmission lines is the occurrence of electrical arcing, stemming from environmental factors, debris, or grid damage. This study delves into the comparative analysis of various architectures of the YOLOv8 model for arc detection. The results showcase the high accuracy of the YOLOv8 m model, demonstrating its robust performance in accurately detecting and localizing objects in complex and dynamic environments. Future research prospects include the utilization of larger datasets to enhance model performance for broader applicability across diverse scenarios in power grid operations.