Valve Detection for Autonomous Water Pipeline Inspection Platform
Rakiba Rayhana, Yutong Jiao, Zhila Bahrami, Zheng Liu, Angie Wu, Xiangjie Kong
Abstract
Water distribution and transmission lines are indispensable to urban infrastructure. The water pipelines are subject to both structural and functional deterioration due to various reasons including aging, negligence, and high demand for water supply. Hence, to ensure a safe and reliable water supply, the water utilities need to perform routine pipe condition assessment. The condition assessment is usually carried out by visual inspection with the machine vision system carried by a robotic platform. The inspection platforms will capture the internal condition of the water pipelines in a video stream. However, the robotic platform frequently experiences difficulties while traversing through the valves installed along the pipeline. This inhibits and disrupts the inspection process of the water pipelines. Therefore, this article proposes a deep learning-based automatic valve detection framework to facilitate the robot’s navigation and ensure continuous inspection without any interruptions. The valve detection model is developed by combining MobileNet-160 and Feature Pyramid Network and is named as MFPN. The developed framework also employs a generative adversarial network to solve the sparse dataset issues and improve the generality of the framework. The comparative study and ablation analyses demonstrate that the proposed framework can achieve a higher mAP value of 89.11% in comparison with the state of the art. Hence, this light weight and efficient solution can be deployed to the robotic platform for real-time valve detection and enable autonomous navigation of the robotic platform for condition assessment of water pipelines.