Review of Deep Learning-Based Methods for Corrosion Detection in Industrial Pipelines

Abstract

Abstract—Corrosion in industrial pipelines is one of the major causes of structural failures, economic losses, and environmental hazards in sectors such as oil, gas, and water distribution. Early detection of corrosion is therefore essential to ensure pipeline safety, reduce maintenance costs, and prevent catastrophic failures. Traditional corrosion monitoring techniques often rely on manual inspection or conventional signal analysis, which can be time consuming, expensive, and sometimes inaccurate when dealing with large-scale pipeline networks. With the rapid advancement of Artificial Intelligence, Deep Learning has emerged as an effective approach for automated defect detection and predictive maintenance.This research focuses on the application of Deep Learning techniques for detecting corrosion in pipelines using imagebased inspection data. High-resolution images obtained from inspection cameras, drones, or robotic systems are used as input for training deep learning models. Convolutional Neural Networks (CNN) are employed to automatically extract features from the images and classify pipeline surfaces as corroded or non-corroded. The methodology includes data preprocessing, image enhancement, feature extraction, and model training using supervised learning techniques. A publicly available corrosion dataset is utilized to train and validate the deep learning model. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1 score. Experimental results demonstrate that deep learning-based approaches can achieve high detection accuracy and provide faster and more reliable corrosion identification compared to traditional inspection methods. The findings of this study highlight the potential of deep learning in improving pipeline monitoring systems and enabling predictive maintenance strategies. By integrating automated corrosion detection into industrial inspection processes, organizations can enhance infrastructure safety, minimize operational risks, and reduce long-term maintenance costs