基于分割注意力与线性变换的轻量化目标检测
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张艳,孙晶雪,孙叶美,刘树东,王传启
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Lightweight object detection based on split attention and linear transformation
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Yan ZHANG,Jing-xue SUN,Ye-mei SUN,Shu-dong LIU,Chuan-qi WANG
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表 2 不同目标检测算法在Pascal VOC数据集上的检测实验结果对比 |
Tab.2 Comparison of experimental results of different target detection algorithms on Pascal VOC dataset % |
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算法 | 主干网络 | AP | mAP | bike | bird | bottle | bus | car | cat | person | sheep | tv | DSSD | ResNet-101 | 84.9 | 80.5 | 53.9 | 85.6 | 86.2 | 88.9 | 79.7 | 78 | 79.4 | 78.6 | DES | VGG-16 | 86.0 | 78.1 | 53.4 | 87.9 | 87.3 | 88.6 | 80.8 | 80.2 | 79.5 | 79.7 | RefineDet | VGG-16 | 85.4 | 81.4 | 60.2 | 86.4 | 88.1 | 89.1 | 82.6 | 82.7 | 79.4 | 80 | R-FCN | ResNet-101 | 87.2 | 81.5 | 69.8 | 86.8 | 88.5 | 89.8 | 81.2 | 81.8 | 79.9 | 80.5 | SPANDet | VGG-16 | 87.5 | 83.3 | 69.7 | 88.7 | 89.2 | 89.1 | 84.7 | 85.6 | 81.5 | 82.6 | 本研究 | CSPDarknet | 94.0 | 86.0 | 80.0 | 91.4 | 93.2 | 92.2 | 90.2 | 89.5 | 84.0 | 85.7 |
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