Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety 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 study proposes an Edge-Aware and Content-Adaptive Feature Fusion Detector (ECAF-Det) for infrared gas leak detection in weak-plume and cluttered thermal scenes. The main methodological contributions of ECAF-Det comprise three task-oriented components. A local--global feature enhancement block preserves fine boundary cues and long-range plume continuity. A multi-scale edge perception module transforms directional-gradient and Gabor-response cues into hierarchical boundary-sensitive structural priors. A content-adaptive sparse routing path aggregation network dynamically regulates multi-scale feature propagation and limits the contribution of less informative cross-scale responses. Experiments on the IIG dataset show that ECAF-Det improves overall and small-plume detection while maintaining moderate computational complexity. On this dataset, ECAF-Det achieves an average precision (AP) of 29.8%, an AP at an IoU threshold of 0.5 AP50 of 84.3%, and a small-object AP of 25.3%. Compared with the Real-Time Detection Transformer with a ResNet-18 backbone (RT-DETR-R18), these values represent improvements of 3.0, 6.5, and 5.4 percentage points, respectively. The model requires 43.7 giga floating-point operations (GFLOPs) and 14.3 M parameters. On the LangGas dataset, ECAF-Det achieves an AP of 36.3% and an AP50 of 68.5%. The AI contribution lies in edge-aware representation learning and content-adaptive sparse feature routing for weak infrared plume perception. The engineering application is automated infrared gas leak detection for industrial safety monitoring, early warning, and remote inspection.