Abstract
Methane emissions from livestock, particularly cattle, significantly contribute to climate change. Effective methane emission mitigation strategies are crucial as the global population and demand for livestock products increase. We introduce Gasformer, a novel semantic segmentation architecture for detecting low-flow rate methane emissions from livestock, and controlled release experiments using optical gas imaging. We present two unique datasets captured with a FLIR GF77 OGI camera. Gasformer leverages a Mix Vision Transformer encoder and a Light-Ham decoder to generate multi-scale features and refine segmentation maps. Gasformer outperforms other state-of-the-art models on both datasets, demonstrating its effectiveness in detecting and segmenting methane plumes in controlled and real-world scenarios. On the livestock dataset, Gasformer achieves mIoU of 88.56%, surpassing other state-of-the-art models. Materials are available at: this http URL.
Abstract (translated)
来自家畜（尤其是牛）的甲烷排放对气候变化具有重要影响。有效的甲烷排放减缓策略至关重要，因为全球人口和畜牧产品需求不断增加。我们介绍了一种名为Gasformer的新型语义分割架构，用于检测家畜中低流量率甲烷排放，并使用光学气体成像进行控制释放实验。我们展示了使用FLIR GF77 OGI相机捕获的两种独特数据集。Gasformer利用Mix Vision Transformer编码器和Light-Ham解码器生成多尺度特征并优化分割图。在控制和现实世界场景中，Gasformer在其他最先进的模型都表现优异，证明了其在检测和分割甲烷浓度的有效性。在畜牧 dataset 上，Gasformer 实现了 mIoU 的88.56%，超越了其他最先进的模型。材料可在以下这个网址找到：http:// this http URL。
URL
https://arxiv.org/abs/2404.10841
