Abstract
Gas Leak Recognition (GLR) is a task where existing techniques face significant challenges, such as the need for infrared cameras, various industrial scenes, and validated data. In this work, We demonstrate Gas Leak Recognition Network (GLRNet), a network with temporal difference inputs and temporal shifting operations. GLRNet integrating module is designed by analogy to human perception, which is a physical constraint in the feature representation. The synergy of our proposed GLRNet and infrared camera we have developed is an emerging comprehensive system that achieves state-of-the-art (SOTA) results in real-world data captured in several chemical industrial parks across the country. Our demo video is at https://youtu.be/glt4DMeNXDU.
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Huang, E., Chen, L., Lv, T., Cao, X. (2022). GLRNet: Gas Leak Recognition via Temporal Difference in Infrared Video. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_41
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