Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (11): 11-20.doi: 10.16180/j.cnki.issn1007-7820.2021.11.002

Previous Articles     Next Articles

UAV Vehicle Target Detection Based on Faster R-CNN

ZHANG Ying,LIU Zilong,WAN Wei   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2020-07-06 Online:2021-11-15 Published:2021-11-16
  • Supported by:
    National Natural Science Foundation of China(61603255)


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