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.

本研究提出了一种完整的半自动腐蚀识别和映射系统，利用最新的基于 LiDAR 的定位和映射方法，结合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。与文献中现有的腐蚀识别系统不同，我们设计的多模式系统成本低廉、便携、半自主，并允许非专业人员收集大规模数据集。在室内实验室环境中进行的一系列实验定量地证明了所采用的 LiDAR 基础三维地图和定位系统的高精度性能，并且当使用我们的像素级手动标注数据集进行训练时，我们的数据驱动语义分割模型的精度达到了约 70%。