基于Faster R-CNN的无人机车辆目标检测

doi:10.16180/j.cnki.issn1007-7820.2021.11.002

Abstract

Abstract:

There are disadvantages such as low resolution, low completeness, and many interference items in the UAV's perspective targets. Additionally, the research progress of UAV target detection system is slow, and its detection accuracy for small targets, incomplete targets and overlapping targets cannot meet the actual needs of society. In view of these problems, this study proposes a vehicle target detection solution for UAV platform based on Faster R-CNN. This solution uses ResNet convolutional neural network as the feature extraction network, improves the network structure, redesigns Anchor generation and improves the Soft-NMS algorithm and other strategies, solves the problem of low detection accuracy of small targets, incomplete targets and overlapping targets, and improves the accuracy of UAV vehicle detection. The test experiments on the dataset constructed in this study show that the proposed algorithm has 13.46% increase in AP value compared with the previous improvement. Test experiments on the public data set show that compared with the current mainstream algorithms, the proposed algorithm has better AP value and recall rate.

Key words: Faster R-CNN, UAV image, vehicle detection, ResNet, convolutional neural network, network structure improvement, Anchor generation, Soft-NMS algorithm

CLC Number:

TP391

ZHANG Ying,LIU Zilong,WAN Wei. UAV Vehicle Target Detection Based on Faster R-CNN[J].Electronic Science and Technology, 2021, 34(11): 11-20.

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数据集	采集设备	分辨率	多场景	车辆图像数目	车辆目标数目
COWC^[10]	航空器	低	是	-	32 716
VEDAI^[11]	卫星	低	是	982	2 950
ITCVD^[12]	飞机	高	是	173	29 088
UCAS-AOD^[13]	卫星	低	是	510	7 114
PUCCR^[14]	摄像头	高	否	2 315	192 216
本文数据集	无人机	高	是	19 522	377 841

Anchor数量	Anchor组合	AP值/%
3	{16²,32²,64²}	91.81
3	{32²,64²,96²}	90.80
3	{64²,96²,128²}	89.19
4	{16²,32²,64²,96²}	93.54
4	{32²,64²,96²,128²}	91.97
5	{16²,32²,64²,96²,128²}	94.03

	α=0.3	α=0.4	α=0.5	α=0.6	α=0.7	α=0.8	α=0.9	α=1.0
改进Soft-NMS算法1	91.57	91.44	91.26	91.81	91.32	91.27	91.31	91.08
改进Soft-NMS算法2	91.32	91.03	91.09	91.47	91.48	91.36	91.01	91.77

算法	AP值/%	召回率/%
Faster R-CNN (VGG16)	74.68	75.58
YOLOv3 (darknet53)	72.55	72.98
SSD (VGG16)	67.69	70.76
R-FCN (ResNet101)	79.42	83.21
本文算法	82.94	86.23

UAV Vehicle Target Detection Based on Faster R-CNN

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