改进YOLOv3的快速遥感机场区域目标检测

doi:10.19665/j.issn1001-2400.2021.05.019

Abstract

Abstract:

The detection of remote sensing airport regional objects is of great military and civilian significance.In order to achieve fast and accurate detection results,a data set that is more mission-specific is independently constructed for the detection task.We use the representative network YOLOv3 of the one-step regression global detection method as the basic framework.For the problem of uneven distribution of categories in the data set,the use of generated data isproposed.The method uses generative adversarial networks to perform targeted data expansion to obtain data sets with domain transformation characteristics and more balanced distribution of different types of data.At the same time,the improved DWFPN detection component is used to fusion deeper distinguishable and more robust features.Experiments show that,compared with the original network,the improved network brings 4.98% increase in mean average precision (mAP) and 8.33% increase in average IOU,which reaches 89.07% mAP and 61.97% average IOU,respectively.At the same time,the average detection time of the improved network is 0.0625s,which is 7 times faster than the RetinaNet-101 network with a similar detection rate.Experiments prove the effectiveness of the network and its practicality for specific tasks.

Key words: object detection, image processing, neural network, airport area, remote sensing

CLC Number:

TP183

HAN Yongsai,MA Shiping,HE Linyuan,LI Chenghao,ZHU Mingming,ZHANG Fei. Detection of the object in the fast remote sensing airport area on the improved YOLOv3[J].Journal of Xidian University, 2021, 48(5): 156-166.

Figures/Tables 17

References 22

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组块	层级	参数
输入端	Inputs layer	(416,416,3)
卷积块1	Conv 32×3×3&Conv 64×3×3_s2	(416,416,32)&(208,208,64)
残差块1	Residual Block 1×64& Conv 128×3×3_s2	(208,208,64)& (104,104,128)
残差块2	Residual Block 2×128& Conv 256×3×3_s2	(104,104,128)& (52,52,256)
残差块3	Residual Block 8×256& Conv 512×3×3_s2	(52,52,256)& (26,26,512)
残差块4	Residual Block 8×512& Conv 1 024×3×3_s2	(26,26,512)& (13,13,1 024)
残差块5	Residual Block 4×1 024& Conv 3×(512×1×1+1 024×3×3)	(13,13,1 024)& (13,13,1 024)

算法	机场	民航飞机	战斗机	直升机	运输机	油罐	平均检测精确率	平均交并比
YOLOv3	0.899 1	0.893 5	0.785 7	0.839 1	0.813 1	0.814 8	0.840 9	0.536 4
YOLOv3+T1+T2	0.909 3	0.913 6	0.878 8	0.928 8	0.878 2	0.835 6	0.890 7	0.619 7

算法	机场	民航飞机	战斗机	直升机	运输机	油罐	平均漏警概率
YOLOv3	0.051 1	0.057 2	0.164 1	0.111 3	0.137 2	0.134 9	0.109 3
YOLOv3+T1+T2	0.040 9	0.037 1	0.070 8	0.020 9	0.072 3	0.114 1	0.059 4
增量	-0.010 2	-0.020 1	-0.093 3	-0.090 4	-0.064 9	-0.020 8	-0.049 9

算法	机场	民航飞机	战斗机	直升机	运输机	油罐	mFA
YOLOv3	0.111 2	0.116 9	0.224 1	0.170 8	0.197 2	0.194 8	0.169 2
YOLOv3+T1+T2	0.101 3	0.096 9	0.131 2	0.081 3	0.132 1	0.173 9	0.119 5
增量	-0.009 9	-0.020 0	-0.092 9	-0.089 5	-0.065 1	-0.020 9	-0.049 7

检测网络	Faster R-CNN	R-FCN	DSSD	YOLOv3	RetinaNet-101	文中网络
平均检测精确率	0.773 4	0.830 2	0.812 5	0.840 9	0.901 2	0.890 7
时间/s	0.215 2	0.167 3	0.163 1	0.058 2	0.396 0	0.062 5

Detection of the object in the fast remote sensing airport area on the improved YOLOv3

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