Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring
arXiv:2512.23234v3 Announce Type: replace-cross Abstract: Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging because gas plumes are often faint, small, semi-transparent, and weakly bounded. This paper proposes an Edge-Aware and Content-Adaptive Feature Fusion Detector (ECAF-Det) for weak-plume detection in cluttered thermal scenes. ECAF-Det integrates three task-oriented designs: a plume-oriented local-global feature enhancement block to preserve fine boundary cues and capture long-range contextual continuity
Advances in AI, particularly in computer vision and deep learning, are enabling more sophisticated and reliable detection systems for complex industrial environments.
This development improves industrial safety and environmental compliance by automating a previously challenging task, reducing human error, and enabling earlier detection of hazardous gas leaks.
The ability to accurately detect faint and semi-transparent gas plumes using AI will lead to more robust and autonomous monitoring systems in industrial settings.
- · Industrial safety and monitoring companies
- · Chemical and oil & gas industries
- · AI/computer vision developers
- · Manufacturers of less advanced detection systems
- · Companies with poor safety records
Reduced industrial accidents and environmental damage related to gas leaks.
Increased regulatory pressure for adoption of advanced AI-driven safety monitoring in high-risk industries.
Integration of similar AI detection capabilities across other difficult-to-monitor industrial processes, driving broader automation in safety and quality control.
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Read at arXiv cs.AI