Acta Armamentarii ›› 2024, Vol. 45 ›› Issue (7): 2085-2096.doi: 10.12382/bgxb.2023.0401
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CHANG Tianqing1, ZHANG Jie1,*(), ZHAO Liyang2, HAN Bin1, ZHANG Lei1
Received:
2023-05-08
Online:
2023-08-18
Contact:
ZHANG Jie
CLC Number:
CHANG Tianqing, ZHANG Jie, ZHAO Liyang, HAN Bin, ZHANG Lei. Research on Armored Vehicle Detection Algorithm Based on Visible and Infrared Image Fusion[J]. Acta Armamentarii, 2024, 45(7): 2085-2096.
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数据集名称 | 图像对数量 | 装甲目标数量 |
---|---|---|
VTAV数据集 | 4977 | 10244 |
训练集 | 3000 | 5784 |
验证集 | 977 | 2209 |
测试集 | 1000 | 2251 |
Table 1 Information of VTAV dataset
数据集名称 | 图像对数量 | 装甲目标数量 |
---|---|---|
VTAV数据集 | 4977 | 10244 |
训练集 | 3000 | 5784 |
验证集 | 977 | 2209 |
测试集 | 1000 | 2251 |
融合检测 结构 | 融合 级别 | 卷积核尺寸 | mAP@0.5/ % | mAP@0.5∶ 0.95/% | |
---|---|---|---|---|---|
1×1 | 3×3 | ||||
EFF | 像素级 | √ | 50.4 | 19.8 | |
EFF | 像素级 | √ | 62.2 | 28.6 | |
MFF | 特征级 | √ | 78.9 | 45.2 | |
MFF | 特征级 | √ | 78.9 | 45.8 | |
LFF | 特征级 | √ | 78.8 | 45.2 | |
LFF | 特征级 | √ | 79.3 | 46.1 |
Table 2 Fusion effects of different convolutional kernel sizes
融合检测 结构 | 融合 级别 | 卷积核尺寸 | mAP@0.5/ % | mAP@0.5∶ 0.95/% | |
---|---|---|---|---|---|
1×1 | 3×3 | ||||
EFF | 像素级 | √ | 50.4 | 19.8 | |
EFF | 像素级 | √ | 62.2 | 28.6 | |
MFF | 特征级 | √ | 78.9 | 45.2 | |
MFF | 特征级 | √ | 78.9 | 45.8 | |
LFF | 特征级 | √ | 78.8 | 45.2 | |
LFF | 特征级 | √ | 79.3 | 46.1 |
融合检测 结构 | 融合 级别 | BN+Relu | mAP@0.5/ % | mAP@0.5∶ 0.95/% |
---|---|---|---|---|
EFF | 像素级 | 78.6 | 43.6 | |
EFF | 像素级 | √ | 62.2 | 28.6 |
MFF | 特征级 | 77.2 | 43.1 | |
MFF | 特征级 | √ | 78.9 | 45.8 |
LFF | 特征级 | 78.1 | 44.1 | |
LFF | 特征级 | √ | 79.3 | 46.1 |
Table 3 Fusion effects of BN layer and Relu activation layer
融合检测 结构 | 融合 级别 | BN+Relu | mAP@0.5/ % | mAP@0.5∶ 0.95/% |
---|---|---|---|---|
EFF | 像素级 | 78.6 | 43.6 | |
EFF | 像素级 | √ | 62.2 | 28.6 |
MFF | 特征级 | 77.2 | 43.1 | |
MFF | 特征级 | √ | 78.9 | 45.8 |
LFF | 特征级 | 78.1 | 44.1 | |
LFF | 特征级 | √ | 79.3 | 46.1 |
BN+Relu | 预训练权重 | mAP@0.5/% | mAP@0.5∶0.95/% |
---|---|---|---|
67.8 | 31.0 | ||
√ | 68.2 | 31.3 | |
√ | 78.6 | 43.6 | |
√ | √ | 62.2 | 28.6 |
Table 4 The influence of pre-trained weights on the structure of early feature fusion
BN+Relu | 预训练权重 | mAP@0.5/% | mAP@0.5∶0.95/% |
---|---|---|---|
67.8 | 31.0 | ||
√ | 68.2 | 31.3 | |
√ | 78.6 | 43.6 | |
√ | √ | 62.2 | 28.6 |
检测结构 | 融合方式 | mAP@0.5/% | mAP@0.5∶0.95/% | mAP@0.5∶0.95精度差 | FPS/(帧·s) | ||
---|---|---|---|---|---|---|---|
Add | Concatenation | RGBT | |||||
FCOS_T | 61.6 | 24.2 | -19.3 | 29.8 | |||
FCOS_RGB | 78.5 | 43.5 | 0 | 29.8 | |||
EFF | √ | 76.5 | 39.6 | -3.9 | 28.8 | ||
EFF | √ | 78.7 | 43.6 | +0.1 | 28.6 | ||
EFF | √ | 72.0 | 39.6 | -3.9 | 28.7 | ||
MFF | √ | 78.9 | 44.5 | +1 | 19.5 | ||
MFF | √ | 78.9 | 45.8 | +2.3 | 13 | ||
LFF | √ | 79.2 | 45.8 | +2.3 | 18.9 | ||
LFF | √ | 79.3 | 46.1 | +2.6 | 18 |
Table 5 Experimental results of different detection models
检测结构 | 融合方式 | mAP@0.5/% | mAP@0.5∶0.95/% | mAP@0.5∶0.95精度差 | FPS/(帧·s) | ||
---|---|---|---|---|---|---|---|
Add | Concatenation | RGBT | |||||
FCOS_T | 61.6 | 24.2 | -19.3 | 29.8 | |||
FCOS_RGB | 78.5 | 43.5 | 0 | 29.8 | |||
EFF | √ | 76.5 | 39.6 | -3.9 | 28.8 | ||
EFF | √ | 78.7 | 43.6 | +0.1 | 28.6 | ||
EFF | √ | 72.0 | 39.6 | -3.9 | 28.7 | ||
MFF | √ | 78.9 | 44.5 | +1 | 19.5 | ||
MFF | √ | 78.9 | 45.8 | +2.3 | 13 | ||
LFF | √ | 79.2 | 45.8 | +2.3 | 18.9 | ||
LFF | √ | 79.3 | 46.1 | +2.6 | 18 |
目标检测算法 | 输入图像 | mAP@0.5/% | mAP@0.5∶0.95/% |
---|---|---|---|
YOLOv5_n | RGB | 75.9 | 44.4 |
YOLOv5_n | RGB+T | 77.2(+1.3) | 45.9(+1.5) |
YOLOv5_s | RGB | 78.4 | 48.6 |
YOLOv5_s | RGB+T | 81.0(+2.6) | 50.5(+1.9) |
YOLOv7_tiny | RGB | 78.5 | 45.7 |
YOLOv7_tiny | RGB+T | 79(+0.5) | 46.5(+0.8) |
YOLOv8_n | RGB | 77.2 | 46.8 |
YOLOv8_n_ | RGB+T | 78.8(+1.6) | 47.7(+0.9) |
Table 7 Results of extended experiments
目标检测算法 | 输入图像 | mAP@0.5/% | mAP@0.5∶0.95/% |
---|---|---|---|
YOLOv5_n | RGB | 75.9 | 44.4 |
YOLOv5_n | RGB+T | 77.2(+1.3) | 45.9(+1.5) |
YOLOv5_s | RGB | 78.4 | 48.6 |
YOLOv5_s | RGB+T | 81.0(+2.6) | 50.5(+1.9) |
YOLOv7_tiny | RGB | 78.5 | 45.7 |
YOLOv7_tiny | RGB+T | 79(+0.5) | 46.5(+0.8) |
YOLOv8_n | RGB | 77.2 | 46.8 |
YOLOv8_n_ | RGB+T | 78.8(+1.6) | 47.7(+0.9) |
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