6 research outputs found
Capturing 3D textured inner pipe surfaces for sewer inspection
Inspection robots equipped with TV camera technology are commonly used to detect defects in sewer systems. Currently, these defects are predominantly identified by human assessors, a process that is not only time-consuming and costly but also susceptible to errors. Furthermore, existing systems primarily offer only information from 2D imaging for damage assessment, limiting the accurate identification of certain types of damage due to the absence of 3D information. Thus, the necessary solid quantification and characterisation of damage, which is needed to evaluate remediation measures and the associated costs, is limited from the sensory side. In this paper, we introduce an innovative system designed for acquiring multimodal image data using a camera measuring head capable of capturing both color and 3D images with high accuracy and temporal availability based on the single-shot principle. This sensor head, affixed to a carriage, continuously captures the sewer's inner wall during transit. The collected data serves as the basis for an AI-based automatic analysis of pipe damages as part of the further assessment and monitoring of sewers. Moreover, this paper is focused on the fundamental considerations about the design of the multimodal measuring head and elaborates on some application-specific implementation details. These include data pre-processing, 3D reconstruction, registration of texture and depth images, as well as 2D-3D registration and 3D image fusion
The role of deep learning in urban water management: A critical review
This is the final version. Available on open access from Elsevier via the DOI in this recordDeep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.Royal SocietyAlan Turing InstituteNational Natural Science Foundation of Chin
Single-pass inline pipeline 3D reconstruction using depth camera array
A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy. In the reconstructed model of a longer pipeline, the proposed method generates the dense 3D surface reconstruction model at the millimeter level accuracy with less than 0.5% distance error. The achieved performance highlights its potential as a useful tool for efficient in-line, non-destructive evaluation of pipeline assets
Simultaneous localization and mapping for inspection robots in water and sewer pipe networks: a review
At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environmen
Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images
The task of detecting and classifying damages in sewer pipes offers an
important application area for computer vision algorithms. This paper describes
a system, which is capable of accomplishing this task solely based on low
quality and severely compressed fisheye images from a pipe inspection robot.
Relying on robust image features, we estimate camera poses, model the image
lighting, and exploit this information to generate high quality cylindrical
unwraps of the pipes' surfaces.Based on the generated images, we apply semantic
labeling based on deep convolutional neural networks to detect and classify
defects as well as structural elements.Comment: Published in: 2018 IEEE Winter Conference on Applications of Computer
Vision (WACV