Detecting infrared gas leaks is critical for environmental monitoring and industrial safety, yet remains difficult because plumes are faint, small, semitransparent, and have weak, diffuse boundaries. We present physics-edge hybrid gas dynamic routing network (PEG-DRNet). First, we introduce the Gas Block, a diffusion-convection unit modeling gas transport: a local branch captures short-range variations, while a large-kernel branch captures long-range propagation. An edge-gated learnable fusion module balances local detail and global context, strengthening weak-contrast plume and contour cues. Second, we propose the adaptive gradient and phase edge operator (AGPEO), computing reliable edge priors from multi-directional gradients and phase-consistent responses. These are transformed by a multi-scale edge perception module (MSEPM) into hierarchical edge features that reinforce boundaries. Finally, the content-adaptive sparse routing path aggregation network (CASR-PAN), with adaptive information modulation modules for fusion and self, selectively propagates informative features across scales based on edge and content cues, improving cross-scale discriminability while reducing redundancy. Experiments on the IIG dataset show that PEG-DRNet achieves an overall AP of 29.8\%, an AP$_{50}$ of 84.3\%, and a small-object AP of 25.3\%, surpassing the RT-DETR-R18 baseline by 3.0\%, 6.5\%, and 5.3\%, respectively, while requiring only 43.7 Gflops and 14.9 M parameters. The proposed PEG-DRNet achieves superior overall performance with the best balance of accuracy and computational efficiency, outperforming existing CNN and Transformer detectors in AP and AP$_{50}$ on the IIG and LangGas dataset.

翻译：红外气体泄漏检测对于环境监测与工业安全至关重要，但由于羽流微弱、尺寸小、呈半透明状且边界模糊扩散，检测任务依然充满挑战。我们提出了物理-边缘混合气体动态路由网络（PEG-DRNet）。首先，我们引入了气体块（Gas Block），这是一个模拟气体输运的扩散-对流单元：其局部分支捕捉短程变化，而大核分支捕捉长程传播。一个边缘门控可学习融合模块平衡了局部细节与全局上下文，从而增强了弱对比度羽流和轮廓线索。其次，我们提出了自适应梯度与相位边缘算子（AGPEO），它通过多方向梯度和相位一致性响应计算可靠的边缘先验。这些先验信息通过一个多尺度边缘感知模块（MSEPM）转化为分层的边缘特征，以强化边界。最后，内容自适应稀疏路由路径聚合网络（CASR-PAN）配备了用于融合和自适应的自适应信息调制模块，能够根据边缘和内容线索，跨尺度选择性地传播信息丰富的特征，从而在提高跨尺度判别能力的同时减少冗余。在IIG数据集上的实验表明，PEG-DRNet实现了29.8%的整体AP、84.3%的AP$_{50}$以及25.3%的小目标AP，分别超过了RT-DETR-R18基线3.0%、6.5%和5.3%，同时仅需43.7 Gflops的计算量和14.9 M的参数。所提出的PEG-DRNet在IIG和LangGas数据集上的AP和AP$_{50}$指标均优于现有的CNN和Transformer检测器，以最佳的精度与计算效率平衡实现了卓越的整体性能。