基于多头自注意力的复杂背景船舶检测算法

于楠晶,范晓飚,邓天民,冒国韬

Ship detection algorithm in complex backgrounds via multi-head self-attention

Nan-jing YU,Xiao-biao FAN,Tian-min DENG,Guo-tao MAO

表 2 不同卷积神经网络的船舶检测结果对比

Tab.2 Comparison of ship detection results of different convolutional neural network

算法 pma/% pa/% OC BCC GCS CS FB PS Faster(VGG16)[21] 90.12 89.44 90.34 90.73 90.87 88.76 90.57 Faster(ResNet18)[21] 90.63 90.37 89.78 90.45 90.91 87.17 88.93 Faster(ResNet50)[21] 91.65 92.38 90.88 92.46 92.91 89.27 90.93 Faster(ResNet101)[21] 92.40 93.68 90.22 93.87 93.41 89.96 91.78 SSD300(MobileNet)[21] 77.66 64.77 76.69 87.43 90.77 71.00 75.32 SSD300(VGG16)[21] 79.37 75.03 76.66 87.66 90.71 71.79 74.35 SSD512(VGG16)[21] 86.73 83.99 83.00 87.08 90.81 85.85 89.65 YOLOv2 random=0[21] 77.51 83.01 79.36 80.60 88.90 62.70 70.48 YOLOv2 random=1[21] 79.06 83.16 82.07 83.21 88.31 64.74 72.89 YOLOv3[23] 87.00 86.00 86.20 87.10 87.10 88.00 90.00 YOLOv4[23] 90.70 90.80 90.70 90.80 90.90 90.60 90.50 MHSA-YOLO 97.59 98.73 98.42 96.41 96.53 98.51 96.94