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This research introduces ECAF-Det, a specialized detector that leverages edge-aware representation learning and content-adaptive sparse routing to significantly improve the detection of faint, small-scale gas plumes in cluttered thermal environments. By dynamically regulating feature propagation to suppress background noise, the model achieves substantial accuracy gains on standard industrial datasets while maintaining reasonable computational complexity. The approach offers a viable visual perception component for enhancing early warning systems in industrial safety monitoring applications.
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; a multi-scale edge perception module that transforms directional gradient and phase-consistency cues into hierarchical edge priors for boundary-sensitive plume representation; and a content-adaptive sparse routing path aggregation network that dynamically regulates multi-scale feature propagation to emphasize informative plume features and suppress redundant background responses. Experiments on the IIG dataset show that ECAF-Det achieves 29.8% AP, 84.3% AP50, and 25.3% small-object AP, improving the RT-DETR-R18 baseline by 3.0, 6.5, and 5.4 percentage points, respectively, with 43.7 GFLOPs and 14.9 M parameters. On the LangGas dataset, ECAF-Det achieves 36.3% AP and 68.5% AP50, demonstrating its generalization to different infrared gas plume appearances. The main AI contribution is edge-aware representation learning with content-adaptive sparse feature routing for weak infrared plume perception. The proposed detector can serve as a visual perception component for early warning and remote inspection in industrial gas leak monitoring.
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; a multi-scale edge perception module that transforms directional gradient and phase-consistency cues into hierarchical edge priors for boundary-sensitive plume representation; and a content-adaptive sparse routing path aggregation network that dynamically regulates multi-scale feature propagation to emphasize informative plume features and suppress redundant background responses. Experiments on the IIG dataset show that ECAF-Det achieves 29.8% AP, 84.3% AP50, and 25.3% small-object AP, improving the RT-DETR-R18 baseline by 3.0, 6.5, and 5.4 percentage points, respectively, with 43.7 GFLOPs and 14.9 M parameters. On the LangGas dataset, ECAF-Det achieves 36.3% AP and 68.5% AP50, demonstrating its generalization to different infrared gas plume appearances. The main AI contribution is edge-aware representation learning with content-adaptive sparse feature routing for weak infrared plume perception. The proposed detector can serve as a visual perception component for early warning and remote inspection in industrial gas leak monitoring.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2512.23234 [cs.CV]
(or arXiv:2512.23234v3 [cs.CV] for this version)
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