阶段 Stage |
操作 Operation |
重复次数 Stack number |
1 | Conv, 7×7, 64, stride 2 Max pool, 3×3, stride 2 |
1 |
2 | Conv, 1×1, 64 Conv, 3×3, 64 Conv, 1×1, 256 |
3 |
3 | Conv, 1×1, 128 Conv, 3×3, 128 Conv, 1×1, 128 |
4 |
4 | Conv, 1×1, 256 Conv, 3×3, 256 Conv, 1×1, |
6 |
5 | Conv, 1×1, 512 Conv, 3×3, 512 Conv, 1×1, |
3 |
ResNet模型结构
本文全文表格
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数据集
Dataset牛只数量
Number of cattlesRGB图像数量
Number of RGB imagesIR图像数量
Number of IR images图像总数量
Total number of images训练集 Training set 60 2570 2002 4572 测试集 Test set 32 1362 1085 2447 -
阶段
Stage操作
Operation重复次数
Stack number1 Conv, 7×7, 64, stride 2
Max pool, 3×3, stride 21 2 Conv, 1×1, 64
Conv, 3×3, 64
Conv, 1×1, 2563 3 Conv, 1×1, 128
Conv, 3×3, 128
Conv, 1×1, 1284 4 Conv, 1×1, 256
Conv, 3×3, 256
Conv, 1×1,1024 6 5 Conv, 1×1, 512
Conv, 3×3, 512
Conv, 1×1,2048 3 -
模型 Model mAP/% CMC-1/% CMC-5/% 未进行跨模态训练的模型 Model without cross-modal training 71.01 75.82 85.82 提出的模型 Proposed model 90.68 94.73 97.82 1) mAP:平均精度均值;CMC-1:一阶累积匹配特征值;CMC-5:五阶累积匹配特征值
1) mAP: Mean average precision; CMC-1: Cumulative match characteristic at rank 1; CMC-5:Cumulative match characteristic at rank 5 -
模型结构
Model structureTriplet注意力机制
Triplet attention
mechanism嵌入扩展模块
Embedding extension
modulesmAP/% CMC-1/% CMC-5/% Parameters/M FLOPs/G 单流
Single-stream77.23 84.55 91.64 9.18 4.73 √ 81.13 86.91 92.36 9.24 4.75 √ 79.00 85.09 92.36 9.18 4.73 √ √ 81.42 87.09 92.91 9.24 4.75 全双流
Full dual-stream80.73 88.73 94.00 9.18 4.73 √ 84.88 87.45 95.09 9.24 4.75 √ 82.75 89.27 93.64 9.18 4.73 √ √ 87.96 91.64 96.17 9.24 4.75 浅层双流
Shallow dual-stream81.86 89.45 94.73 9.18 4.73 √ 89.60 92.00 95.09 9.24 4.75 √ 86.19 89.64 96.55 9.18 4.73 √ √ 90.68 94.73 97.82 9.24 4.75 1) mAP:平均精度均值;CMC-1:一阶累积匹配特征值;CMC-5:五阶累积匹配特征值;Parameters:参数量;FLOPs:浮点运算量
1) mAP: Mean average precision; CMC-1: Cumulative match characteristic at rank 1; CMC-5:Cumulative match characteristic at rank 5; Parameters: Number of parameters; FLOPs: Floating point operations