Humans　can　easily　distinguish　glass　in　indoor　and　outdoor　scenes.　However,　when　robots　work　indoors　or　outdoors,　they　struggle　to　perceive　the　presence　of　glass,　relying　on　sensors　accurately.　They　may　mistakenly　interpret　objects　reflected　by　the　glass　as　obstacles　and　even　fail　to　detect　the　presence　of　glass,　leading　to　collisions　and　potential　hazards.　Accurately　identifying　transparent　objects　like　glass　in　the　environment　has　become　a　challenging　problem　in　robot　environment　perception　tasks.　This　paper　first　introduces　the　characteristics　of　previous　glass　recognition　methods.　A　lightweight　RGB-Infrared　fusion　glass　detection　network　IRGNet　was　designed　based　on　low　power　consumption　requirements　and　high　real-time　performance　for　mobile　robots.　The　network　includes　an　information　fusion　module　(IFM)　integrating　complementary　feature　information　from　RGB　and　infrared　images　at　multiple　scales.　We　conducted　experiments　on　Trans10k　and　the　self-made　dataset　IRG　dataset.　The　experimental　results　demonstrate　that　this　network　outperforms　previous　detection　speed　and　accuracy　methods.　©　2023　IEEE.