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The study aims to enhance hazard recognition in high-risk operations using advanced computer vision techniques.

June 4, 2026Applied SciencesOpen Access

Improved YOLOv8-Based Real-Time Detection Method for Illegal Behaviors in Oil and Gas High-Risk Operations

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Randomized trial demonstrates real-time identification of hazards in high-risk operations, indicating enhanced safety protocols.

The study aims to enhance hazard recognition in high-risk operations using advanced computer vision techniques.
Collected 1.4 million images from high-risk petroleum sites
Developed 40 video recognition algorithms integrating object detection and pose estimation
Incorporated spatiotemporal attention mechanisms for improved accuracy on small targets.
Achieved an accuracy of ≥90% in detecting violation behaviors
Enabled intelligent identification of hazards like personnel under cranes and at heights without safety harnesses
Facilitated transformation in risk management from human-based to an integrated defense model.

Tian et al. (2026) studied this question.

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