Compared　with　Fast　R-CNN[1],　YOLO[2-4]　and　other　detection　algorithms[5],　SSD　algorithm[6]　has　better　detection　accuracy　and　real-time　performance　detection　performance.　However,　the　comprehensive　detection　performance　of　SSD　algorithm　is　strong,　but　it　still　has　some　shortcomings　in　network　structure,　model　training　and　other　aspects.　Therefore,　aiming　at　the　problems　of　large　network　calculation,　low　multi-target　detection　accuracy　and　unbalanced　feature　learning　in　the　application　of　SSD　algorithm　to　multi-target　detection　and　classification,　this　paper　proposes　an　Adaptive　Feature　Fusion(AFF)　structure　that　can　be　applied　to　lightweight　detection　algorithm,　which　includes　Channel　Adaptive　Weighting　(CAW)module　and　Spatial　Feature　Aggregation　(SFA)　module.　And　based　on　the　optimization　of　rate　strategy　learning,　the　AFSSDMV2(Adaptive　FSSD　with　MobileNetV2)　algorithm　is　proposed.　©　2025,　John　Wiley　and　Sons　Inc.　All　rights　reserved.