DeepPipeNet enables deep learning based anomaly detection for oil and gas pipeline monitoring

Oil and gas pipelines are critical infrastructures that facilitate the transportation of energy resources over vast distances. However, they are prone to various operational threats, including leaks, corrosion, and mechanical failures, which can lead to severe environmental damage and financial losses. Traditional monitoring systems often struggle with real-time anomaly detection, necessitating advanced AI-driven solutions for improved pipeline integrity and risk mitigation. This study introduces DeepPipeNet, a hybrid deep learning-based ensemble framework designed to detect pipeline anomalies and failures with high precision. The proposed methodology integrates three state-of-the-art Convolutional Neural Networks (CNNs)—VGG16, ResNet50, and DenseNet121—to extract rich and diverse feature representations. These features are fused through a concatenation mechanism, followed by an attention module to emphasize critical patterns, and finally classified via a Meta-CNN architecture consisting of multi-path dense layers with softmax activation. DeepPipeNet was rigorously evaluated on two domain-specific datasets, the Oil Pipeline Accidents Dataset (focusing on multi-category cause classification) and Oil and Gas Pipline Leakage dataset focused on Corrosion Severity (categorized into high, medium, and low severity levels). After rigorous hyperparameter tuning to optimize generalization, the framework achieved outstanding test accuracy of 98.29% and 98.51%, respectively. These results demonstrate DeepPipeNet’s superior capability in detecting pipeline-related anomalies with high precision (exceeding 98%), significantly minimizing false positives and enabling near-real-time monitoring. By leveraging deep feature fusion and attention-driven refinement, DeepPipeNet offers a scalable and robust AI-based solution that advances predictive maintenance strategies and ensures safer, more efficient pipeline operations in the oil and gas industry.