Identification of potential external breakage of construction work areas in transmission corridors based on DETR

Abstract Construction work such as large-scale machinery may lead to transmission line failures and endanger the safe work of power grids. A DETR-based method for identifying the potential damage caused by construction work in transmission corridors is proposed, which combines the advantages of convolutional neural network (CNN) and transformer. Firstly, the ResNet-50 network is deployed to extract the image features of the construction work areas. Then, the DETR network with encoder-decoder structure is used for multi-scale training and prediction to obtain the identification results of the external breakage potentials in the construction areas of transmission corridors. Experiments show that the average accuracy (AP) of monitoring and identifying the construction work areas using the DETR network reaches 0.856, which can accurately identify the potential hazards in the construction work areas and provide data for the safe work of transmission corridors.