基于多尺度融合与注意力机制的小目标车辆检测
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李凯,林宇舜,吴晓琳,廖飞宇
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Small target vehicle detection based on multi-scale fusion technology and attention mechanism
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Kai LI,Yu-shun LIN,Xiao-lin WU,Fei-yu LIAO
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表 4 各类方法在PASCAL VOC数据测试中的AP值 |
Tab.4 AP value of various methods in PASCAL VOC data test % |
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算法模型 | aero | bike | bird | boat | bottle | bus | car | cat | chair | cow | FasterRcnn | 84.9 | 79.8 | 79.8 | 74.3 | 53.9 | 77.5 | 75.9 | 88.5 | 45.6 | 77.1 | CenterNet | 81.0 | 75.0 | 66.0 | 52.0 | 43.0 | 78.0 | 80.0 | 87.0 | 59.0 | 72.0 | SSD | 83.1 | 84.7 | 74.0 | 69.6 | 49.5 | 85.4 | 86.2 | 85.2 | 60.4 | 81.5 | RP-SSD | 88 | 83.8 | 74.8 | 73.2 | 48.9 | 83.9 | 86.8 | 91.0 | 63.2 | 81.9 | DSSD | 83.6 | 85.2 | 74.5 | 70.1 | 50.4 | 85.6 | 86.7 | 85.6 | 61.0 | 82.1 | FSSD | 84.9 | 86.4 | 74.8 | 63.3 | 50.6 | 84.6 | 87.9 | 86.9 | 63.1 | 83.2 | YOLOv4 | 83.6 | 84.0 | 73.8 | 59.2 | 72.2 | 91.0 | 90.0 | 70.7 | 60.9 | 64.9 | YOLOv5 | 84.2 | 87.6 | 65.9 | 63.3 | 77.0 | 80.2 | 91.5 | 83.7 | 66.5 | 66.4 | OURS | 89.8 | 89.8 | 85.4 | 75.5 | 61.5 | 82.5 | 87.5 | 90.5 | 73.9 | 95.6 | | 算法模型 | table | dog | horse | mbike | person | plant | sheep | sofa | train | tv | FasterRcnn | 55.3 | 86.9 | 81.7 | 80.9 | 79.6 | 40.1 | 72.6 | 60.9 | 81.2 | 61.5 | CenterNet | 54.0 | 81.0 | 70.0 | 68.0 | 74.0 | 41.0 | 71.0 | 58.0 | 82.0 | 70.0 | SSD | 75.1 | 82.0 | 85.9 | 85.3 | 77.7 | 49.6 | 76.1 | 80.0 | 87.4 | 74.4 | RP-SSD | 76.3 | 81.2 | 85.3 | 84.6 | 79.3 | 63.5 | 78.9 | 83.4 | 87.9 | 73.9 | DSSD | 75.4 | 82.5 | 86.2 | 85.4 | 78.6 | 51.2 | 75.9 | 80.5 | 86.7 | 75.1 | FSSD | 76.8 | 83.1 | 85.0 | 83.2 | 77.3 | 57.9 | 78.4 | 82.1 | 86.5 | 73.2 | YOLOv4 | 67.3 | 89.6 | 77.4 | 65.2 | 86.0 | 47.7 | 77.4 | 72.3 | 82.6 | 83.3 | YOLOv5 | 59.8 | 82.8 | 86.6 | 83.1 | 85.4 | 56.4 | 70.3 | 62.9 | 87.9 | 90.8 | OURS | 78.4 | 90.7 | 89.5 | 82.1 | 75.6 | 63.1 | 81.5 | 93.9 | 89.7 | 85.9 |
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