The detection of high pressure steam leakage in power plant is related to the long-term stable operation of power plant equipment. In order to improve the accuracy of high-pressure steam leakage detection in power plants and solve the problem of wrong segmentation and leakage segmentation of leakage areas, this paper proposes a high-pressure steam leakage detection algorithm based on CBAM-Res_UNet image segmentation network. The residual_block of ResNet is added to the UNet structure to obtain more semantic information of leakage images, and CBAM is integrated to strengthen the learning of regional characteristics of high-pressure steam leakage images. According to the influence of different loss functions and evaluation criteria on image segmentation results, the loss function Focal Loss+Dice Loss and performance index F1_score are selected. Through the experiment on the image data set of highpressure steam leakage in power plant, the F1_score obtained by CBAM-Res_UNet network is 0. 985. The experimental results show that the network can more completely segment the steam leakage area, and has a strong generalization ability for the variety of high pressure steam leakage images.