Marine　litter　can　cause　significant　damage　to　marine　biodiversity,　threatening　the　marine　food　chain　and　spreading　harmful　substances,　posing　a　significant　impact　on　the　ocean　ecosystem.　Autonomous　Underwater　Vehicles　(AUVs)　can　automatically　remove　marine　litter　using　sensors　and　trained　models.　this　paper　evaluates　the　YOLOv7　series　of　models　that　utilize　deep　learning　to　detect　targets　in　a　real　underwater　environment.　In　order　to　improve　the　performance　of　the　model,　we　introduced　two　attention　mechanisms,　and　the　experimental　results　showed　a　2.5%　increase　in　Mean　Average　Precision(mAP)　values.　We　used　a　large　publicly　available　dataset　of　re-annotated　open-water　debris　to　train　convolutional　neural　networks　for　target　detection,　and　we　evaluated　the　trained　models　on　a　subset　of　the　dataset,　to　provide　insights　into　the　ability　of　deep　learning　to　detect　marine　litter.　In　addition,　to　prevent　attacks　by　malicious　actors　during　AUVs　cloud　platform　access,　we　introduced　data　encryption　protection　to　ensure　that　the　model＇s　predicted　results　can　be　correctly　received　by　AUVs.　©　2023　ACM.