The　YOLOv4　neural　network　is　employed　for　underwater　target　recognition.　To　improve　the　accuracy　and　speed　of　recognition,　the　structure　of　YOLOv4　is　modified　by　replacing　the　upsampling　module　with　a　deconvolution　module　and　by　incorporating　depthwise　separable　convolution　into　the　network.　Moreover,　the　training　set　used　in　the　YOLO　network　is　preprocessed　by　using　a　modified　mosaic　augmentation,　in　which　the　gray　world　algorithm　is　used　to　derive　two　images　when　performing　mosaic　augmentation.　The　recognition　results　and　the　comparison　with　the　other　target　detectors　demonstrate　the　effectiveness　of　the　proposed　YOLOv4　structure　and　the　method　of　data　preprocessing.　According　to　both　subjective　and　objective　evaluation,　the　proposed　target　recognition　strategy　can　effectively　improve　the　accuracy　and　speed　of　underwater　target　recognition　and　reduce　the　requirement　of　hardware　performance　as　well.