With the continuous expansion of the scale of the national grid, overhead conductors, as an important component of the power system, have become extremely important to its regular inspection. At the same time, with the opening of the low-altitude flight field, the identification of overhead conductors has also become extremely important to ensure the normal operation of the national grid and the safety of low-altitude flight. The method for identifying overhead wires in infrared aerial images was proposed using the Deeplabv3+ semantic segmentation network model, and the algorithm was improved according to the characteristics of infrared overhead wire image targets in this paper. Firstly, an attention mechanism was added to the feature extraction backbone network ResNet50 of the original Deeplabv3+ algorithm, so that the network highlights the characteristics of the area where the wire target was located, and paid more attention to the location of the wire target, thereby weakening the background and other non-main area features; Then, the encoder part of Deeplabv3+ was improved, and the Feature Pyramid Networks (FPN) was added to the ResNet50 model, which can fuse the shallow and deep features, enhance the network's ability to identify the attributes of targets of different sizes, and the performance of small targets such as wires, and then improve the overall recognition effect of the network. The experimental results show that the improved algorithm has good detection performance, the average pixel accuracy is 93.52%, and the average intersection ratio is 87.83%.