Gasformer: A Transformer-based Architecture for Segmenting Methane
  Emissions from Livestock in Optical Gas Imaging
Toqi Tahamid Sarker Mohamed G Embaby Khaled R Ahmed Amer AbuGhazaleh
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.