| 模型Model | P | R | mAP |
| BiFPN+SE | 76.1 | 83.1 | 87.0 |
| BiFPN+CBAM | 77.5 | 81.5 | 87.6 |
| BiFPN+CA | 78.6 | 81.3 | 88.1 |
| BiFPN+Triplet | 79.3 | 83.2 | 88.4 |
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BiFPN添加不同注意力机制的模型试验结果对比
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Triplet注意力机制不同插入位置的模型试验结果对比
模型1)Model P R mAP F1 YOLOv5s 75.4 83.6 86.5 79.29 A+ CIoU+Focal(9) 77.3 81 85.6 79.11 A+CIoU+Focal(9+25) 80.8 79.9 86.2 80.35 A+CIoU+Focal 75.9 85.2 87.1 80.28 A+CIoU+Focal(24) 81.1 85.8 90.0 83.38 1) A：YOLOv5s-BiFPN-Triplet，括号内数字表示Triplet插入位置。
1) The number in parentheses indicated the insertion position of Triplet. -
BiFPN添加不同注意力机制的模型试验结果对比
模型Model P R mAP BiFPN+SE 76.1 83.1 87.0 BiFPN+CBAM 77.5 81.5 87.6 BiFPN+CA 78.6 81.3 88.1 BiFPN+Triplet 79.3 83.2 88.4 -
消融试验结果
BiFPN Triplet Focal-CIoU P R mAP 75.4 83.6 86.5 √ 80.3 84.3 88.0 √ 76.4 86.1 89.3 √ 78.4 83.0 89.8 √ √ 79.3 83.2 88.4 √ √ 77.5 84.5 89.6 √ √ 80.1 84.0 88.9 √ √ √ 81.1 85.8 90.0 -
Focal-CIoU与其他损失函数对比
模型1)Model P R mAP F1 A 79.3 83.2 88.4 81.20 A+ DIoU 79.1 84.8 89.2 81.85 A+ DIoU+Focal 79.4 84.3 89.6 81.78 A+ SIoU 80.1 85.6 89.8 82.76 A+ SIoU+Focal 77.9 85.3 88.5 81.43 A+ EIoU 78.7 84.3 88.7 81.40 A+ EIoU+Focal 80.7 80.0 88.1 80.35 A+ WIoU 81.1 79.4 88.2 80.24 A+CIoU+Focal 81.1 85.8 90.0 83.38 1) A：YOLOv5s-BiFPN-Triplet. -
BTF-YOLOv5与其他模型的对比
模型
ModelP/% R/% mAP/% 模型大小/MB
Model sizeF1/% 参数
ParameterGFLOPs1) SSD 76.3 81.7 84.3 95.5 78.91 2.49×107 31.4 YOLOv3 71.8 83.8 86.5 123.5 77.34 3.30×107 78.1 YOLOv4 84.2 65.6 76.6 256.3 73.75 6.50×107 142.3 YOLOv5s 75.4 83.6 86.5 14.4 79.29 7.03×106 16.0 YOLOv7 79.6 83.1 87.1 142.1 81.31 3.72×107 105.1 YOLOv8n 81.7 76.6 87.4 6.2 79.07 3.01×106 8.2 YOLOv8s 73.6 83.0 87.2 22.5 78.02 1.11×107 28.6 YOLOv9 86.0 78.8 89.7 102.8 82.24 5.10×107 238.9 BTF-YOLOv5s 81.1 85.8 90.0 14.7 83.38 7.17×106 16.7 1) GFLOPs：每秒10亿次的浮点运算数。
1) GFLOPs: Giga floating-point operations per second.
