Dual-domain feature fusion attention network for leak detection in water supply pipes

Abstract Accurate detection of the size of pipeline leaks is the key to minimizing economic and resource infrastructure loss. However, extracting critical information about different leak sizes from complex pipeline signals is a major challenge. To address this difficult problem, this paper proposed a water pipe leakage detection method based on a Dual-domain feature fusion attention network (DFFAN). It employs time series imaging to fuse images generated under different transform domains of the original signal to achieve high-accuracy detection of leak size. Specifically, by fusing the Gramian Angular Field (GAF) matrix with the Continuous Wavelet Transform (CWT) image of the original signal, DFFAN can comprehensively capture the temporal, amplitude, and time-frequency information in the signal. The designed attention fusion module can optimally allocate weights to features according to their importance and further enhance the leakage features through weighted fusion. The effectiveness of the method is evaluated using the collected leakage data. The experimental results show that the proposed method can effectively recognize the four leakage states in two material pipes. It has better detection performance compared with advanced pipeline leakage detection methods.