A Complete System for Automated Semantic–Geometric Mapping of Corrosion in Industrial Environments

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, radiographic 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 three-dimensional (3D) point-cloud-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 the previous corrosion identification systems available in the literature, which are either intrusive (e.g., electrochemical testing) or based on costly equipment (e.g., ultrasonic sensors), our designed multi-modal vision-based system is low cost, portable, and semi-autonomous and allows the collection of large datasets by untrained personnel. A set of experiments performed in relevant test environments demonstrated quantitatively the high accuracy of the employed 3D mapping and localization system, using a light detection and ranging (LiDAR) device, with less than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.05</mn></mrow></semantics></math></inline-formula> m and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.02</mn></mrow></semantics></math></inline-formula> m average absolute and relative pose errors. Also, our data-driven semantic segmentation model was shown to achieve <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>70</mn><mo>%</mo></mrow></semantics></math></inline-formula> precision in corrosion detection when trained with our pixel-wise manually annotated dataset.