Infrared image segmentation technology for electrical equipment based on genetic algorithm and fuzzy C-Class mean

With the growth of electricity demand and the expansion of power grid scale in China, thermal fault identification of electrical equipment in substations plays a crucial role in ensuring the safe and efficient operation of the power grid and preventing accidents. Infrared imaging technology can identify abnormal temperature areas based on the surface temperature distribution of equipment through non-contact, long-distance, and non-stop methods, thereby identifying thermal faults of equipment without the need for power outage. Due to the increasing resolution of infrared images collected by modern infrared detection equipment, traditional image segmentation techniques are difficult to meet real-time requirements. Therefore, this article proposes an image segmentation algorithm that combines genetic algorithm and fuzzy C-means clustering. It improves the selection, crossover, and mutation operators of genetic algorithm and combines them with fuzzy C-means clustering, effectively avoiding the disadvantage of traditional FCM being sensitive to random initial values and easily trapped in local optima. Experiments have shown that this algorithm accelerates convergence speed, improves image segmentation accuracy, improves global search ability, and can effectively improve image segmentation accuracy.