Physics-Inspired Modeling and Content Adaptive Routing in an Infrared Gas Leak Detection Network

• URL: http://arxiv.org/abs/2512.23234v1
• Date: Mon, 29 Dec 2025 06:28:20 GMT
• Title: Physics-Inspired Modeling and Content Adaptive Routing in an Infrared Gas Leak Detection Network
• Authors: Dongsheng Li, Chaobo Chen, Siling Wang, Song Gao,
• Abstract summary: We present a physics-edge hybrid gas dynamic routing network (PEG-DRNet) for detecting infrared gas leaks.<n>PEG-DRNet achieves superior overall performance with the best balance of accuracy and computational efficiency.
• Score: 19.83756107644484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Spectral Gating Networks [65.9496901693099]
  We introduce Spectral Gating Networks (SGN) to introduce frequency-rich expressivity in feed-forward networks.<n>SGN augments a standard activation pathway with a compact spectral pathway and learnable gates that allow the model to start from a stable base behavior.<n>It consistently improves accuracy-efficiency trade-offs under comparable computational budgets.
  arXiv  Detail & Related papers  (2026-02-07T20:00:49Z)
• Cross-Modal Alignment and Fusion for RGB-D Transmission-Line Defect Detection [11.637942429146172]
  This paper proposes CMAFNet, a Cross-Modal Alignment and Fusion Network that integrates RGB appearance and depth geometry through a principled-then-fuse paradigm.<n> CMAFNet consists of a Semantic Recomposition Module that performs dictionary-based feature purification.<n>A lightweight variant reaches 24.8% mAP50 at 228 FPS with only 4.9M parameters, surpassing all YOLO-based detectors.
  arXiv  Detail & Related papers  (2026-02-02T06:11:33Z)
• TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder [66.22997415145467]
  This paper presents a joint completion and detection framework that improves the detection feature in sparse areas.<n> Specifically, we propose TransBridge, a novel transformer-based up-sampling block that fuses the features from the detection and completion networks.<n>The results show that our framework consistently improves end-to-end 3D object detection, with the mean average precision (mAP) ranging from 0.7 to 1.5 across multiple methods.
  arXiv  Detail & Related papers  (2025-12-12T00:08:03Z)
• DFIR-DETR: Frequency Domain Enhancement and Dynamic Feature Aggregation for Cross-Scene Small Object Detection [16.16000521213211]
  Small object detection in UAV remote sensing images is difficult.<n>Current transformer-based detectors struggle with three critical issues.<n>We introduce DFIR-DETR to tackle these problems through dynamic feature aggregation combined with frequency-domain processing.
  arXiv  Detail & Related papers  (2025-12-08T01:25:10Z)
• DualGazeNet: A Biologically Inspired Dual-Gaze Query Network for Salient Object Detection [52.32976488996896]
  We introduce DualGazeNet, a pure Transformer framework for salient object detection.<n>Experiments on five RGB benchmarks show that DualGazeNet consistently surpasses 25 state-of-the-art CNN- and Transformer-based methods.
  arXiv  Detail & Related papers  (2025-11-24T08:08:22Z)
• StripRFNet: A Strip Receptive Field and Shape-Aware Network for Road Damage Detection [5.980259319825554]
  Road surface damage threatens traffic safety and hinders sustainable urban development.<n>We propose StripRFNet, a novel deep neural network comprising three modules.<n>Experiments on the RDD2022 benchmark show that StripRFNet surpasses existing methods.
  arXiv  Detail & Related papers  (2025-10-17T18:01:48Z)
• Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications [5.026196568145574]
  This paper presents the first comprehensive benchmarking of federated learning strategies for throughput prediction in realistic 5G edge scenarios.<n>It is found that FedBN consistently delivers robust performance under non-IID conditions.<n>LSTM and Transformer models outperform CNN-based baselines by up to 80% in R2 scores.
  arXiv  Detail & Related papers  (2025-08-11T21:27:40Z)
• An Efficient Aerial Image Detection with Variable Receptive Fields [0.0]
  We propose a transformer-based detector incorporating three key components.<n>VRF-DETR achieves 51.4% mAPtextsubscript50 and 31.8% mAPtextsubscript50:95 with only 13.5M parameters.
  arXiv  Detail & Related papers  (2025-04-21T15:16:13Z)
• PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport [56.56430888985025]
  We propose a generative adversarial network for point cloud quality enhancement (PCE-GAN)<n>The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit.<n>The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction.
  arXiv  Detail & Related papers  (2025-02-26T07:34:33Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff [26.566339984225756]
  Existing salient object detection methods often adopt deeper and wider networks for better performance. We propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches. We show that our method achieves better efficiency-accuracy balance across five benchmarks.
  arXiv  Detail & Related papers  (2023-01-17T03:43:25Z)
• DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers [105.74546828182834]
  We show a hardware-efficient dynamic inference regime, named dynamic weight slicing, which adaptively slice a part of network parameters for inputs with diverse difficulty levels. We present dynamic slimmable network (DS-Net) and dynamic slice-able network (DS-Net++) by input-dependently adjusting filter numbers of CNNs and multiple dimensions in both CNNs and transformers.
  arXiv  Detail & Related papers  (2021-09-21T09:57:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.