2024/04/16 by Toqi Tahamid Sarker, Sarker, Toqi Tahamid, Mohamed G. Embaby +4 · 3 citations
Chemistry · Engineering · Environmental Science · #Advanced Chemical Sensor Technologies #Atmospheric and Environmental Gas Dynamics #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Spectroscopy and Laser Applications
paper · pdf · doi:10.48550/arxiv.2404.10841
openalex publication_date 2024/04/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.