基于YOLOv4和自适应锚框调整的谷穗检测方法
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郝王丽, 尉培岩, 郝飞, 韩猛, 韩冀皖, 孙玮蓉, 李富忠
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Foxtail Millet Ear Detection Approach Based on YOLOv4 and Adaptive Anchor Box Adjustment
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Wangli HAO, Peiyan YU, Fei HAO, Meng HAN, Jiwan HAN, Weirong SUN, Fuzhong LI
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Table 2 The impact of IOU values on the performance of different models
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model | IOU | Precision/% | Recall/% | F1/-score% | mAP/% |
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YOLOv2 | 0.20 | 85.00 | 81.00 | 83.00 | 84.05 | 0.35 | 83.00 | 80.00 | 81.00 | 81.03 | 0.50 | 77.00 | 73.00 | 75.00 | 71.52 | 0.65 | 53.00 | 51.00 | 52.00 | 40.64 | YOLOv3 | 0.20 | 91.00 | 81.00 | 86.00 | 84.05 | 0.35 | 90.00 | 81.00 | 85.00 | 82.36 | 0.50 | 86.00 | 77.00 | 81.00 | 76.96 | 0.65 | 65.00 | 59.00 | 62.00 | 48.55 | YOLOv4 | 0.20 | 92.00 | 84.00 | 87.00 | 85.01 | 0.35 | 91.00 | 83.00 | 87.00 | 83.69 | 0.50 | 87.00 | 79.00 | 83.00 | 78.99 | 0.65 | 70.00 | 64.00 | 67.00 | 56.38 |
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