Intelligent Detection of Transmission Line Hazards Using Video Image Analysis Techniques

Abstract

A new approach to power line upkeep has emerged in recent years: automated inspections driven by computer vision. In order to keep power transmission dependable, safe, and sustainable, it is now necessary to use a large collection of pictures and videos. Recent studies have shown that deep learning approaches may significantly improve power line inspection operations. Manual inspection is the gold standard for transmission line safety detection, but it's slow, susceptible to human error, and constrained by inspection cycles, ambient conditions, and the level of expertise of the inspectors doing the checks. Identifying and alerting of transmission line abnormalities in real-time is challenging, and there are substantial constraints and safety dangers to consider. A novel solution that combines the intuitive image recognition benefits of video surveillance with the high-precision range and speed measurement capacities of modern radar detection technology has just emerged: lightning fusion technology. The two datasets are intelligently merged and analyzed via the use of sophisticated data processing methods. This article delves into a lightning vision fusion–based intelligent monitoring method for transmission lines and suggests a system that uses deep learning (DL) algorithms to automatically record and analyze changes in the surrounding environment of transmission lines, greatly enhancing the accuracy and timeliness of such monitoring. In addition to reducing the need for human involvement and operating expenses, the experimental findings demonstrate that this system successfully prevents missed and false alerts, offering a stronger technical assurance for the reliable and secure functioning of the power system.