To address the problems of few and unbalanced data samples in the visual detection of water supply pipeline leakage in energy power plants, an intelligent detection algorithm for water supply pipeline leakage based on small sample unbalanced data was proposed. First, a data enhancement method based on Multi-mask mix was proposed. The original image was extracted and mixed by the mask layer randomly generated, and the support vector machine (SVM) was incorporated into Multi-mask mix to obtain pipeline normal and leakage features, thus providing more accurate prior labels for the hybrid mask blocks. Secondly, an equalization strategy was proposed and applied to the image level and mask level to achieve data equalization. Finally, a deep learning-based Resnet18 network model was utilized to attain pipeline leak detection and identification. The experimental results show that the algorithm can improve the accuracy of the Resnet18 model for pipeline leakage detection by 1.1%–4.4% after processing image data, and can effectively enhance the classification accuracy of the deep learning model for pipeline leakage detection, outperforming other existing algorithms. In addition, the algorithm has now been successfully applied to the leakage detection of water supply pipelines in energy power plants.