A review of computer vision applications for asset inspection in the oil and gas industry

Abstract

This review explores the current application of computer vision (CV) technologies in the inspection of pipelines within the oil and gas industry, highlighting the methodologies, challenges, and advancements in this critical area. Through a systematic analysis of key articles, our study emphasizes CV’s role in addressing crucial issues such as corrosion, leaks, oil spills, and mechanical damage, areas identified as critical through our literature review. Predominant CV techniques like object detection and image segmentation, particularly using advanced frameworks like You Only Look Once (YOLO), Mask Region-based Convolutional Neural Network (R-CNN), and U-Net, showcase the field’s robust response to asset inspection challenges. Additionally, our findings reveal a significant reliance on in-house or directly acquired datasets, primarily through RGB and thermal imaging or increasingly via internet and satellite resources, underscoring the urgent need for standardized, accessible datasets to advance CV research. Despite these advancements, a gap in real-world testing remains, indicating a pressing need for field validation to ensure the operational viability of CV applications in asset inspection. In conclusion, this study reaffirms the transformative potential of CV technologies in enhancing asset integrity and operational safety across the oil and gas industry. However, the findings also highlight critical challenges, such as the scarcity of standardized datasets and the need for more comprehensive field testing. Looking ahead, future research should focus on expanding the application of CV, fostering collaborative dataset development, and ensuring that these technologies can bridge the gap between theoretical research and practical implementation, ultimately contributing to more reliable and efficient asset inspection.