Applications of YOLO in the Oil and Gas Industry

Authors: Ahmad Alotaibi, Masnour Asiri, Naif Qahtani, Abdullah Aldawsari

Abstract: The You Only Look Once (YOLO) algorithm has become one of the most widely used deep learning frameworks for real-time object detection due to its high detection accuracy, computational efficiency, and ability to simultaneously perform object localization and classification. This paper reviews the applications of YOLO in industrial environments, with particular emphasis on battery leakage detection, facial detection for plant access control, and hazardous gas leakage detection. These applications demonstrate how YOLO enhances manufacturing quality, strengthens industrial security, improves workplace safety, and supports intelligent automation through rapid and reliable object detection. Despite its significant advantages, the practical implementation of YOLO presents several challenges. The model requires substantial computational resources and specialized hardware to achieve real-time performance, particularly when processing high-resolution images or multiple video streams. Furthermore, YOLO relies heavily on large, diverse, and accurately annotated datasets, while class imbalance in industrial datasets can reduce detection performance for rare but safety-critical objects. To overcome these limitations, several solutions are discussed, including lightweight YOLO architectures, model compression techniques, edge artificial intelligence (AI) hardware acceleration, data augmentation, synthetic data generation, balanced sampling methods, and advanced loss functions. Overall, the continuous evolution of YOLO architectures and optimization techniques has significantly improved its applicability across industrial sectors, making YOLO a promising and adaptable solution for smart manufacturing, industrial inspection, surveillance, and safety monitoring within Industry 4.0 environments.

DOI: http://doi.org/10.5281/zenodo.21406036