In　recent　years,　vision　technology　has　been　increasingly　utilized　in　environmental　monitoring,　particularly　in　air　quality　prediction　and　smoke　detection.　Despite　advancements,　there　remains　a　need　for　efficient　models　capable　of　accurate　real-time　detection　on　resource-constrained　devices.　This　paper　presents　dual-channel　dual-mode　smoke　detection　network　(DDSNet),　a　novel　dual-channel　architecture　designed　for　detecting　industrial　smoke　from　single　images.　DDSNet　leverages　high-frequency　and　low-frequency　channels　to　capture　detailed　and　broad-spectrum　features,　respectively.　Extensive　experiments　on　the　D-fire　and　Fire-Flame-Dataset　demonstrate　that　DDSNet　achieves　better　performance　with　an　accuracy　of　98.30%　and　99.49%　respectively,　while　maintaining　low　inference　time　and　parameters.　Our　ablation　study　confirms　the　effectiveness　of　the　dual-channel　structure　in　enhancing　detection　accuracy　without　significantly　increasing　computational　complexity,　making　DDSNet　suitable　for　deployment　on　mobile　devices.　©　2024　Copyright　held　by　the　owner/author(s).