Deep learning technology driven identification and monitoring of hidden dangers in transmission lines

With the continuous expansion of China's power grid coverage, the operating environment of transmission lines is becoming increasingly complex, and traditional manual inspection methods are inefficient and pose safety hazards. This article proposes a "low-power+deep learning" fusion technology, designs a low-power intelligent image acquisition terminal, and combines a lightweight deep recognition model to construct an all-weather automated transmission line hidden danger recognition system. The system adopts a distributed "end edge cloud" collaborative architecture, including image acquisition terminals, edge processing modules, wireless transmission modules, and remote recognition centers. Through optimized YOLOv5s model and image enhancement technology, the system exhibits good hazard recognition performance in multi climate scenarios, and its applicability and stability in power consumption, fault detection, and rainy weather resistance have been experimentally verified. The results show that the system significantly outperforms traditional manual inspection methods in terms of hazard detection rate, coverage range, and all-weather operation capability, providing effective support for intelligent operation and maintenance of transmission lines in complex environments.