Deep Learning and Remote Sensing for Intelligent Damage Detection in Industrial Pipelines

Pipeline systems are vital components of modern industrial infrastructure, ensuring the safe and efficient transportation of fluids such as oil, gas, and chemicals. However, these systems are prone to damage from mechanical stress, corrosion, temperature fluctuations, and environmental factors. Among these, cracks represent the most common form of damage and, if left undetected, can lead to severe environmental hazards, operational downtime, and significant economic losses. Traditional inspection methods, while effective, are often time-consuming, costly, and pose risks to human safety. To address these challenges, unmanned aerial vehicles (UAVs) equipped with advanced remote sensing technologies have emerged as promising alternatives for pipeline monitoring. UAVs offer enhanced safety, cost-effectiveness, and efficiency, enabling the rapid detection and assessment of pipeline damage. This study focuses on comparing two prominent object detection algorithms, SSD and YOLOv7, for pipeline damage detection. Both algorithms were tested under varying environmental conditions and pipeline structures to evaluate their accuracy, speed, and reliability. The results demonstrated that SSD achieved a precision of 0.719, a recall of 0.656, and an F1 score of 0.686. In contrast, YOLOv7 outperformed SSD in recall (0.677) and F1 score (0.690), highlighting its superior ability to detect defects. While the speed and simplicity of SSD make it well-suited for real-time applications, YOLOv7’s balanced performance and higher recall make it the preferred choice for scenarios where missing defects is unacceptable. These findings underscore the potential of integrating UAVs with advanced detection algorithms for more effective and reliable pipeline monitoring.