Jung Jan Thomas, Reiterer Alexander
Department of Sustainable Systems Engineering, University of Freiburg, Georges-Köhler-Allee 10, 79110 Freiburg im Breisgau, Germany.
Fraunhofer Institute for Physical Measurement Techniques IPM, Georges-Köhler-Allee 301, 79110 Freiburg im Breisgau, Germany.
Sensors (Basel). 2024 Dec 5;24(23):7786. doi: 10.3390/s24237786.
The maintenance and inspection of sewer pipes are essential to urban infrastructure but remain predominantly manual, resource-intensive, and prone to human error. Advancements in artificial intelligence (AI) and computer vision offer significant potential to automate sewer inspections, improving reliability and reducing costs. However, the existing vision-based inspection robots fail to provide data quality sufficient for training reliable deep learning (DL) models. To address these limitations, we propose a novel multi-sensor robotic system coupled with a DL integration concept. Following a comprehensive review of the current 2D (image) and 3D (point cloud) sewage pipe inspection methods, we identify key limitations and propose a system incorporating a camera array, front camera, and LiDAR sensor to optimise surface capture and enhance data quality. Damage types are assigned to the sensor best suited for their detection and quantification, while tailored DL models are proposed for each sensor type to maximise performance. This approach enables the optimal detection and processing of relevant damage types, achieving higher accuracy for each compared to single-sensor systems.
下水道管道的维护和检查对城市基础设施至关重要，但目前仍主要依赖人工，资源消耗大且容易出现人为错误。人工智能（AI）和计算机视觉技术的进步为下水道检查自动化提供了巨大潜力，可提高可靠性并降低成本。然而，现有的基于视觉的检查机器人无法提供足够的数据质量来训练可靠的深度学习（DL）模型。为解决这些限制，我们提出了一种结合DL集成概念的新型多传感器机器人系统。在全面回顾当前二维（图像）和三维（点云）污水管道检查方法后，我们确定了关键限制，并提出了一种包含相机阵列、前置摄像头和激光雷达传感器的系统，以优化表面捕捉并提高数据质量。将损坏类型分配给最适合检测和量化它们的传感器，同时针对每种传感器类型提出定制的DL模型以最大化性能。这种方法能够对相关损坏类型进行最佳检测和处理，与单传感器系统相比，每种损坏类型的检测精度更高。
