Abstract
Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radio-graphic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems, and state-of-the-art computer vision methods for its identification. In this work, we propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in LiDAR-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques, in order to build semantic-geometric maps of industrial environments. Unlike previous corrosion identification systems available in the literature, our designed multi-modal system is low-cost, portable, semi-autonomous and allows collecting large datasets by untrained personnel. A set of experiments in an indoor laboratory environment, demonstrate quantitatively the high accuracy of the employed LiDAR based 3D mapping and localization system, with less then $0.05m$ and 0.02m average absolute and relative pose errors. Also, our data-driven semantic segmentation model, achieves around 70\% precision when trained with our pixel-wise manually annotated dataset.
Abstract (translated)
腐蚀是一种自然发生的导致金属材料退化的过程，在工业环境中对质量控制和金属物体保存而言，需要进行严谨的检测。传统的腐蚀识别技术，包括超声波测试、无线电graphic测试和磁通泄漏，需要在现场部署昂贵且笨重的设备以实现有效数据采集。一种未探索的替代方法是采用轻量级、传统的相机系统和最先进的计算机视觉技术来进行腐蚀识别和绘制。在这项工作中，我们提出了一个工业环境中的半自动腐蚀识别和绘图系统。我们利用了最近在基于激光雷达的定位和绘图方法以及基于视觉的语义分割深度学习技术，构建了工业环境的语义-几何地图。与文中的 previous corrosion identification systems 不同，我们所设计的 multimodal system 成本低、便携、半自动化，并允许非受过训练的人员收集大量数据。在室内实验室环境下进行的一系列实验，证明了所采用的基于激光雷达的3D 定位和定位系统的高准确性，平均绝对和相对姿态误差小于 $0.05m$ 和 $0.02m$。此外，我们的数据驱动语义分割模型，在用我们逐像素手动标注的数据集上进行训练时，实现了约 70\% 的精确度。
URL
https://arxiv.org/abs/2404.13691
