Intelligent and autonomous pipeline deposit tracking based on a multi-object tracking framework
Louis Ngai Yuen Wong, Sihao Yu, Kazaf Yuen Pan Wong · Tunnelling and Underground Space Technology · 2026
Pipelines play a significant role in transferring energies, materials and fulfilling public needs. However, conventional pipeline maintenance approaches predominantly depend on human inspection of captured closed circuit television (CCTV) records, a process that is particularly labor-intensive and time-consuming for lengthy pipelines. To address these limitations, this study proposes an autonomous framework based on the multi-object tracking (MOT) algorithm for efficient and accurate deposit detection and tracking within pipelines, significantly reducing the need for manual intervention. The proposed MOT model has been trained and validated on a customized pipe CCTV dataset, consisting of more than 12,000 video frames. The experimental results indicate that the combination of YOLOX (for detection) and BYTE (for tracking) achieves the highest MOTA, IDF1 and HOTA among all the tested models, with values of 87.4 %, 90.1 % and 78.7 %, respectively. Further testing conducted on a real-world sewer pipeline project demonstrates the robustness of our model. The estimation error of the deposit location predicted by the MOT model is less than ± 0.1 m, with a mean absolute error of only 0.06 m. These findings highlight the substantial advantages of the autonomous MOT system over manual methods, including improved efficiency, consistent accuracy and reduced labor demands, thus demonstrating its reliability and significant application potential for practical engineering practice.