Development and Application of Edge Intelligent Monitoring Device for Hidden Danger of Transmission Channel Based on AI Chip

Tower cranes, excavators and other external damage hazards lead to frequent transmission channel accidents. Effectively detecting the external damage hazards around the transmission channel is of great significance to ensure the safe and stable operation of the transmission line. Therefore, based on the edge intelligent chip, an intelligent edge detection device for the hidden danger of transmission channel is developed, and a lightweight hidden danger identification method suitable for the front-end device with limited computing resources is proposed. Firstly, the visual feature of the transmission channel image is extracted by using the depth residual network, and then the candidate area of the hidden danger target is captured by using the candidate area production network RPN, Then the full convolution neural network FCN is used to classify and locate the hidden danger of external damage. Finally, the actual collected transmission channel images are constructed into a sample set for model test and experimental verification. The experimental results show that the proposed method shows good applicability in the edge device.