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

The accumulation of heat in high-voltage cable tunnels during long-term operation is very obvious, which can have serious consequences for the safety and stability of power supply. Traditional infrared imaging is an effective means of monitoring high-temperature faults, but image processing methods that rely on traditional image information may result in false or missed detections under background noise pollution. With the development of deep learning, techniques such as convolutional networks, multi-scale modeling, and attention mechanisms have provided new possibilities for heatmap recognition. This article constructs a hybrid deep learning model that combines spatiotemporal features and regional sensitivity to achieve precise positioning and intelligent warning of high-temperature areas. It completes a high-precision, low redundancy, and reliable intelligent alarm system for infrared temperature difference defect recognition, which has wide practicality.