Object　detection　has　achieved　great　progress　and　is　used　in　various　tasks.　However,　detecting　small　objects　with　lack　of　appearance　information　is　still　a　challenging　task.　It　is　found　that　even　if　the　training　set　with　rich　images　is　used　to　train　a　network,　the　small　objects　in　the　image　with　different　background　complexity　cannot　be　well　detected.　To　address　the　above　issue,　this　paper　proposes　a　novel　small　object　detection　framework　based　on　a　background　complexity　classification　strategy　specific　to　the　contradiction　by　adopting　the　idea　of　＂divide　and　rule＂　in　philosophy.　Firstly,　a　Background　Complexity　Classification　Network　(BCCResNet)　is　proposed　to　coarsely　classify　the　input　images　into　three　categories　according　to　the　complexity　of　their　background　textures.　Then,　a　detection　network　with　parallel　structure　is　designed　by　using　mainstream　models　to　detect　small　objects　for　three　categories　of　images.　Extensive　experiments　are　conducted　on　two　small　object　detection　datasets,　i.e.,　AI-TOD　and　DOTAv1.0.　Our　proposed　method　can　significantly　improve　AP　of　small　object　detection,　showing　effectiveness　and　advantages.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2024.