Infrared　small　target　detection　is　a　technique　for　finding　small　targets　from　infrared　clutter　background.　Previous　deep-learning-based　approaches　have　achieved　promising　results.　However,　the　lack　of　high-level　semantic　information　leads　to　a　degradation　of　small　infrared　target　features　in　the　deeper　layers　of　the　neural　network,　resulting　in　suboptimal　representation　capabilities.　To　address　this　issue,　we　propose　an　Infrared　Low-level　Network　(ILNet)　that　conceptualizes　infrared　small　targets　as　salient　regions　characterized　by　limited　semantic　information.　In　contrast　to　other　state-of-the-art　methods,　ILNet　emphasizes　low-level　information　more　significantly,　rather　than　treating　it　uniformly　with　high-level　information.　A　lightweight　feature　fusion　module,　named　Interactive　Polarized　Orthogonal　Fusion　module　(IPOF),　is　proposed,　which　integrates　more　important　low-level　features　from　the　shallow　layers　into　the　deep　layers.　A　Dynamic　One-Dimensional　Aggregation　layers　(DODA)　are　inserted　into　the　IPOF,　to　dynamically　adjust　the　aggregation　of　low　dimensional　information　according　to　the　number　of　input　channels.　In　addition,　the　idea　of　ensemble　learning　is　used　to　design　a　Representative　Block　(RB)　to　dynamically　allocate　weights　for　shallow　and　deep　layers.　Experimental　results　on　the　challenging　NUAA-SIRST　(78.22%　nIoU　and　1.33　×　10−6Fa)　and　IRSTD-1k　(68.91%　nIoU　and　3.23　×　10−6Fa)　dataset　demonstrate　that　the　proposed　ILNet　can　get　better　performances　than　other　SOTA　methods.　Moreover,　ILNet　can　obtain　a　greater　improvement　with　the　increasement　of　data　volume.　Training　code　are　available　at　https://github.com/LiHaoqing/ILNet.　©　1965-2011　IEEE　ALL　RIGHT　RESERVED.