研究文章
Multibranch对象检测方法对交通场景
|
| 模型 |
阈值 |
| 0.1 |
0.15 |
0.2 |
0.25 |
0.3 |
0.35 |
0.4 |
0.45 |
0.5 |
0.55 |
0.6 |
0.65 |
|
| RCNN [4] |
72.45 |
71.12 |
68.67 |
66.89 |
63.21 |
61.72 |
59.56 |
56.01 |
53.29 |
50.67 |
46.59 |
41.21 |
| 快RCNN [9] |
78.23 |
77.67 |
76.55 |
74.31 |
71.29 |
69.11 |
67.47 |
65.12 |
62.89 |
59.61 |
57.23 |
54.45 |
| SSD (15] |
84.67 |
82.58 |
80.69 |
78.97 |
76.29 |
75.17 |
73.90 |
70.55 |
68.22 |
65.14 |
62.25 |
58.30 |
| 面具RCNN [13] |
80.34 |
79.25 |
78.58 |
78.10 |
77.31 |
75.69 |
74.26 |
72.79 |
70.99 |
68.68 |
64.36 |
60.01 |
| SINet [2] |
88.90 |
88.08 |
82.29 |
81.81 |
80.22 |
76.21 |
73.33 |
68.68 |
66.43 |
64.42 |
63.99 |
60.68 |
| YOLOv3 [17] |
85.87 |
84.01 |
81.44 |
79.45 |
77.39 |
75.88 |
72.32 |
69.73 |
68.97 |
65.82 |
64.29 |
61.33 |
| MBNet网 |
89.25 |
87.27 |
84.45 |
82.66 |
79.99 |
77.11 |
74.77 |
71.38 |
68.20 |
66.23 |
65.01 |
61.58 |
|
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