基于YOLOv5s的无人机密集小目标检测算法
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韩俊,袁小平,王准,陈烨
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UAV dense small target detection algorithm based on YOLOv5s
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Jun HAN,Xiao-ping YUAN,Zhun WANG,Ye CHEN
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| 表 3 不同算法在VisDrone2021数据集上的平均精度和平均精度均值 |
| Tab.3 Average precision and mean average precision for different algorithms on VisDrone2021 dataset |
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| 算法 | AP/% | mAP $ {}_{50} $/% | | A | B | C | D | E | F | G | H | I | J | | TridentNet[16] | 22.8 | 9.0 | 5.3 | 46.2 | 30.7 | 25.5 | 21.3 | 16.0 | 39.0 | 17.9 | 43.1 | | RRNet[17] | 30.5 | 14.8 | 14.1 | 51.5 | 35.8 | 35.2 | 28.8 | 19.0 | 45.0 | 26.0 | 55.0 | | CenterNet[18] | 28.0 | 12.0 | 8.9 | 51.2 | 35.9 | 27.5 | 21.0 | 19.8 | 37.7 | 20.9 | 48.5 | | YOLOv5+head | — | — | — | — | — | — | — | — | — | — | 33.8 | | YOLOv5+upsampling | — | — | — | — | — | — | — | — | — | — | 50.5 | | YOLOv5+ M-Bi | — | — | — | — | — | — | — | — | — | — | 43.6 | | YOLOv4[19] | 25.0 | 13.1 | 8.5 | 64.2 | 22.5 | 22.6 | 11.5 | 8.0 | 44.5 | 22.0 | 43.0 | | YOLOv3-LITE[20] | 34.6 | 22.9 | 8.0 | 71.2 | 31.4 | 22.1 | 15.5 | 7.1 | 41.3 | 32.7 | 41.9 | | MSC-CenterNet[21] | 33.5 | 15.3 | 12.5 | 55.2 | 40.6 | 32.0 | 29.2 | 21.6 | 42.5 | 27.4 | 39.5 | | Faster R-CNN[22] | 21.0 | 14.7 | 7.5 | 51.0 | 30.2 | 19.6 | 15.7 | 9.5 | 31.6 | 20.3 | 33.2 | | LSA_YOLO | 37.2 | 25.4 | 18.5 | 58.6 | 35.7 | 35.8 | 29.4 | 21.5 | 47.2 | 28.4 | 57.6 |
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