Infrared image early warning method for the overheating of high-voltage cable tunnels on the basis of hybrid deep learning

In high-voltage cable tunnels, the complex environment affects infrared image segmentation and cable overheating detection accuracy, resulting in high false warning rates. This paper proposes a hybrid deep learning-based infrared image early warning method. It uses dual mapping transformation for image registration, combines AlexNet and residual networks for segmentation, optimizes detection with hyper-parameters, fuses model results to determine overheating points, and sets early warning and transmission radius. Experiments show it precisely identifies overheating, reduces the false warning rate to no more than 1%, and effectively warns of faults.