基于改进Swin Transformer的舰船目标实例分割算法

钱坤, 李晨瑄, 陈美杉, 郭继伟, 潘磊

Ship target instance segmentation algorithm based on improved Swin Transformer

Kun QIAN, Chenxuan LI, Meishan CHEN, Jiwei GUO, Lei PAN

表4 算法对比结果

Table 4 Algorithm comparison results

算法 主干网络 mAPsegm/% AP50segm/% AP75segm/% 参数量/MB FPS FCN[6] VGG16 62.2 79.5 69.2 134 13.3 Mask R-CNN[5] ResNet-50 69.4 89.6 77.9 110 15.0 Cascade Mask R-CNN[30] ResNet-50 68.7 89.4 76.3 82 18.0 YOLACT++[8] ResNet-50 66.3 82.3 74.6 129 32.6 基线算法 Swin-Ting 73.9 90.8 87.5 86 15.3 本文算法 改进Swin-Ting 75.4 91.3 89.4 89 15.5