Leak Detection of Underground Water Pipelines Using Acoustic Feature Extraction

Accurate detection of leaks in underground water pipelines poses significant challenges due to the complexity of acoustic signals and environmental noise. The deep burial and widespread distribution of water supply pipelines in urban areas make current manual detection methods inefficient and time-consuming. This paper introduces the deep semi-supervised anomaly detection multiscale (DSMS) algorithm, which combines a multi-scale convolutional network with the deep semi-supervised anomaly detection (deep SAD) framework to address these challenges. The multi-scale convolutional network is specifically designed to extract both fine-grained and global acoustic features from Mel frequency cepstral coefficients (MFCCs), enabling effective differentiation between leakage sounds and background noise. Additionally, a pre-training module based on an autoencoder initializes the network weights, improving convergence and increasing the evaluation metric by 27%. The DSMS algorithm achieves state-of-the-art performance, with a test accuracy of 99.14% and a false positive rate of 0.89%. This approach offers a promising solution for efficient and precise leak detection in urban water distribution systems.