Water pipe leakage detection method based on bi-sensor data fusion.

In water supply pipe leakage detection, a single type of sensor data makes it difficult to describe the pipeline's operational status comprehensively, resulting in a high false detection rate when determining the extent of leakage. Therefore, this paper proposes a leakage detection method based on the Improved Bi-sensor Gramian Angular Field (IBGAF) and Dual-channel Multiscale Feature Fusion Network (DMFFN). The method can fully utilize the complementary information of the two sensors and improve the reliability of leak detection. In IBGAF, the two types of sensor data are mapped as points in a spherical coordinate system. The inverse cosines of their normalized amplitudes are set as the polar and azimuthal angles, respectively. Based on the positional correlation of the points, three IBGAF matrices are computed to encode the R, G, and B color channels of the image for signal fusion. The DMFFN adopts a multi-scale convolutional architecture combined with the attention mechanism, which can fully mine the complementary information of the fused signals. The designed 2-D convolutional parallel feature fusion mechanism performs adaptive fusion of multi-scale information, effectively reducing network complexity. The proposed method is compared with the single-class signal detection-based method, and the experimental results demonstrate that the method can significantly enhance detection accuracy. Compared with other detection methods, the method has the best performance and can accurately recognize four leakage conditions of pipelines.