Detection and Visualization of Corroded Surfaces Using Machine Learning

Abstract

The use of artificial intelligence in asset management greatly assists the industry and structural health monitoring systems. Using machine learning techniques for asset inspections can increase safety, reduce access costs, provide objective classification, and improve digital asset management systems. The detection and visualization of corrosion from digital images present significant advantages like automation, access to remote locations, mitigation of risk of inspectors, cost savings, and detecting speed. This paper used deep learning convolutional neural networks to build simple corrosion detection models and used an extreme gradient boosting algorithm to visualize the corroded surfaces. A large dataset of 1900 images with corrosion and without corrosion was collected using web scraping techniques and labeled accordingly. Training a deep learning model requires massive and high-resolution image datasets and intensive image labeling to approach high-level accuracy. The results and findings will improve the development of deep learning models for detecting and visualizing specific features on corroded surfaces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.