@article{Ma2026, 
author = {Ying-Han Ma and Hong Zhao},
title = {UAV-based pipeline leakage detection via acoustics with owl auditory-inspired OPEI representation},
year = {2026},
journal = {Petroleum Science},
volume = {23},
number = {9},
pages = {5908-5913},
keywords = {Pipeline leakage detection, Unmanned aerial vehicle, OPEI, Acoustic signal processing, Bionics},
url = {https://www.sciopen.com/article/10.1016/j.petsci.2026.07.024},
doi = {10.1016/j.petsci.2026.07.024},
abstract = {To address the low sensitivity of visual UAV inspection in leakage detection for above-ground natural gas pipelines, an acoustic detection method inspired by the owl auditory mechanism was developed. An innovative oscillatory positional encoding imaging (OPEI) technique was proposed, through which one-dimensional acoustic signals were converted into two-dimensional image representations incorporating time-domain, frequency-domain, and amplitude information. Recognition accuracy of 97.38% for OPEI images on the testing dataset was achieved by the EfficientNetV2 cascade model. The results demonstrated that the biomimetic design enhanced acoustic signal sensitivity and enabled fine-grained recognition of pipeline leakage apertures (1/5/10 mm). This study proposes a novel acoustic signal processing method, providing a theoretical foundation for ensuring the reliability and safety of natural gas pipeline transportation.}
}