目标检测强化上下文模型
郑晨斌,张勇,胡杭,吴颖睿,黄广靖

Object detection enhanced context model
Chen-bin ZHENG,Yong ZHANG,Hang HU,Ying-rui WU,Guang-jing HUANG
表 6 各目标检测器在VOC2007测试集上的完整检测结果
Tab.6 Complete detection results of each object detector on VOC2007 test set
方法 $\varPhi $/% aero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tv
注:部分论文中没有给出VOC2007测试集上的完整检测结果,1)网络模型是本文使用对应论文公开发布的权重文件的检测结果.
Faster R-CNN[19] 73.17 76.5 79.0 70.9 65.5 52.1 83.1 84.7 86.4 52.0 81.9 65.7 84.8 84.6 77.5 76.7 38.8 73.6 73.9 83.0 72.6
ION[20] 75.55 79.2 83.1 77.6 65.6 54.9 85.4 85.1 87.0 54.4 80.6 73.8 85.3 82.2 82.2 74.4 47.1 75.8 72.7 84.2 80.4
R-FCN[21] 79.51 82.5 83.7 80.3 69.0 69.2 87.5 88.4 88.4 65.4 87.3 72.1 87.9 88.3 81.3 79.8 54.1 79.6 78.8 87.1 79.5
SSD300*[12] 77.51 79.5 83.9 76.0 69.6 50.5 87.0 85.7 88.1 60.3 81.5 77.0 86.1 87.5 84.0 79.4 52.3 77.9 79.5 87.6 76.8
${\rm{DSOD30}}{{\rm{0}}}$ 1) 77.66 80.5 85.5 76.7 70.9 51.5 87.4 87.9 87.1 61.7 79.3 77.1 83.2 87.1 85.6 80.9 48.5 78.7 80.2 86.7 76.7
DSSD321[12] 78.63 81.9 84.9 80.5 68.4 53.9 85.6 86.2 88.9 61.1 83.5 78.7 86.7 88.7 86.7 79.7 51.7 78.0 80.9 87.2 79.4
${\rm{FSSD}}{300}$ 1) 78.77 82.3 85.8 78.2 73.6 56.8 86.3 86.4 88.1 60.3 85.8 77.7 85.3 87.7 85.4 79.9 54.1 77.9 78.7 88.4 76.7
RefineDet320[24] 79.97 83.9 85.4 81.4 75.5 60.2 86.4 88.1 89.1 62.7 83.9 77.0 85.4 87.1 86.7 82.6 55.3 82.7 78.5 88.1 79.4
${\rm{RFB}}\,{\rm{Net}}{300}$ 1) 80.42 83.7 87.6 78.9 74.8 59.8 88.8 87.5 87.9 65.0 85.0 77.1 86.1 88.4 86.6 81.7 58.1 81.5 81.2 88.4 80.2
ECMNet300 80.52 83.9 88.3 79.9 73.1 61.8 88.7 87.9 87.8 64.1 85.7 78.9 86.2 88.5 86.9 82.4 56.8 79.6 81.3 88.4 80.2