Due　to　the　complexity　of　rural　river　environment,　the　performance　of　traditional　YoloV5　model　for　target　detection　is　insufficient.　Considering　that　the　river　environment　includes　river　drifts　and　river　slope　accumulation,　they　have　different　characteristics　but　have　correlation.　Therefore,　the　target　detection　of　river　image　includes　two　tasks:　identifying　the　drifts　on　the　river　channel　and　identifying　the　accumulation　on　the　river　slope.　In　addition,　there　is　no　uniform　standard　to　design　the　shared　layer　when　using　deep　learning　model　for　multi　task　learning.　The　cross-stitch　network　can　decide　the　best　sharing　layer　through　end-to-end　learning,　and　the　cross-stitch　unit　can　stitch　two　networks.　Therefore,　we　introduce　the　cross-stitch　unit　into　the　backbone　layer　of　YoloV5　for　the　first　time,　which　makes　the　YoloV5　network　model　a　multi　task　learning　model.　At　the　same　time,　we　use　gamma　correction　method　to　preprocess　the　river　drifts　image　set　to　improve　the　detection　accuracy　of　the　model.　We　took　field　photos　of　rural　rivers　and　collected　similar　photos　on　the　Internet　to　train　and　evaluate　the　model.　The　experimental　results　show　that　the　average　accuracy　(map)　of　the　proposed　method　is　78.6%　and　78.0%　respectively,　which　is　2.1%　and　2.3%　higher　than　YoloV5　model,　and　better　than　the　traditional　target　recognition　model.　©　2021　IEEE.