Timely　detection　of　swimming　accidents　plays　a　critical　role　in　keeping　the　safety　of　infants　in　pools.　However,　the　targets　in　the　infant　drowning　detection　are　generally　small,　densely-distributed　and　often　immersed　in　complicated　environment　with　lighting　various　and　toy　disturbances,　and　thus　there　are　currently　few　efficient,　accurate　and　comprehensive　solutions　for　such　a　task.　Aiming　at　this　problem,　a　novel　intelligent　real-time　framework　for　detecting　infant　drowning　is　proposed.　Processing　of　the　framework　includes　two　stages:　in　stage　I,　an　attention　mechanism-integrated　YOLOv5-alike　model　is　designed　to　extract　infant　portrait　foreground,　which　can　reduce　the　interferences　caused　by　background　noise　and　improve　the　accuracy　of　detecting　tiny　objects.　On　this　basis,　in　stage　II,　Single　Shot　Multi-box　Detector　(SSD)　is　utilized　to　filter　out　the　falsely　-detected　targets　in　stage　I　and　recognize　swimming　posture　accurately.　Live　videos　are　collected　from　ten　infant　swimming　pools　to　generate　training　dataset　of　totally　7,296　swimming　images　and　7,723　portrait　images　of　infants　for　stage　I　and　II　respectively.　And　a　set　of　1,222　infant　swimming　images　from　another　two　pools　are　annotated　to　test　the　trained　framework.　Experimental　results　show　that　the　Mean　Average　Precision　(mAP)　of　the　framework　is　97.17%,　and　the　processing　speed　can　reach　43　frames　per　second,　which　outperforms　any　previous　related　network　and　has　significant　application　value　in　practical　infant　drowning　detection.　Additionally,　a　web-based　platform　is　developed　to　put　the　framework　into　practice,　whose　preliminary　test　results　demonstrate　its　strong　application　potential.