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: github.com/toqitahamid/Gasformer.

通过 Gasformer 以及光学气体成像技术，我们成功引入了一种新的语义分割方法，用于检测来自畜牧业低流量甲烷排放。在两个数据集上，Gasformer 表现出优于其他先进模型的性能，证明其在受控和真实场景中检测和分割甲烷羽流的有效性，从畜牧业数据集上的结果来看，Gasformer 达到了88.56%的 mIoU，超过了其他先进模型。

Gasformer：用于光学气体成像中分割畜禽排放的基于Transformer的架构