轻量级模型CarboFormer高效精准识别气体成像中的二氧化碳泄漏。
CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging
- 采用优化编码器-解码器结构，融合多尺度特征与辅助监督。
- 在两个数据集上分别达到84.88%和92.98%的mIoU，参数仅5.07M。
- 适合无人机等资源受限设备实时监测，尤其擅长低流量场景。
二氧化碳（CO₂）排放是环境影响及多种工业过程（如畜牧管理）的重要指标。本文提出CarboFormer，一种面向光学气体成像（OGI）的轻量级语义分割框架，用于检测和量化不同应用场景下的CO₂排放。该方法结合优化的编码器-解码器架构、多尺度特征融合与辅助监督策略，有效建模气体羽流图像中的局部细节与全局关系，同时保持极低计算开销，适用于资源受限环境。我们构建了两个新数据集：(1) 受控二氧化碳释放（CCR）数据集，模拟不同流量（10–100 SCCM）的泄漏；(2) 实时安科姆（RTA）数据集，聚焦奶牛瘤胃液体外实验的排放。大量实验表明，CarboFormer在CCR数据集上达84.88% mIoU，RTA数据集上达92.98% mIoU，参数量仅5.07M，推理速度达84.68 FPS。其在低流量条件下表现尤为突出，显著优于SegFormer-B0（83.36%）和SegNeXt（82.55%），适用于编程无人机等实时监测平台。本工作推动了环境感知与精准畜牧管理的发展。
原文摘要 · Abstract (English)
Carbon dioxide (CO$_2$) emissions are critical indicators of both environmental impact and various industrial processes, including livestock management. We introduce CarboFormer, a lightweight semantic segmentation framework for Optical Gas Imaging (OGI), designed to detect and quantify CO$_2$ emissions across diverse applications. Our approach integrates an optimized encoder-decoder architecture with specialized multi-scale feature fusion and auxiliary supervision strategies to effectively model both local details and global relationships in gas plume imagery while achieving competitive accuracy with minimal computational overhead for resource-constrained environments. We contribute two novel datasets: (1) the Controlled Carbon Dioxide Release (CCR) dataset, which simulates gas leaks with systematically varied flow rates (10-100 SCCM), and (2) the Real Time Ankom (RTA) dataset, focusing on emissions from dairy cow rumen fluid in vitro experiments. Extensive evaluations demonstrate that CarboFormer achieves competitive performance with 84.88\% mIoU on CCR and 92.98\% mIoU on RTA, while maintaining computational efficiency with only 5.07M parameters and operating at 84.68 FPS. The model shows particular effectiveness in challenging low-flow scenarios and significantly outperforms other lightweight methods like SegFormer-B0 (83.36\% mIoU on CCR) and SegNeXt (82.55\% mIoU on CCR), making it suitable for real-time monitoring on resource-constrained platforms such as programmable drones. Our work advances both environmental sensing and precision livestock management by providing robust and efficient tools for CO$_2$ emission analysis.
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