Skip Navigation Links
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


Object Recognition for Remote Sensing Images from UAVs via Convolutional Neural Networks

Journal of Vibration Testing and System Dynamics 5(4) (2021) 337--343 | DOI:10.5890/JVTSD.2021.12.002

Jiefu Li$^{1,2}$, Xi Liu$^{3}$, Mingtao Liu$^{1,2}$, Lijia Cao$^{1,2,4 }$

$^{1}$ Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, 643000, Sichuan, China

$^{2}$ School of Automation and Engineering, Sichuan University of Science & Engineering, Zigong, 643000, Sichuan, China

$^{3}$ Unit 32033 of PLA, Haikou, 570100, Hainan, China

$^{4}$ Sichuan Key Provincial Research Base of Intelligent Tourism, Zigong 643000, Sichuan, China

Download Full Text PDF



In recent years, researches of remote sensing data have begun to develop towards mass information and diversification, and traditional analytical methods alone could not meet the needs of modern data processing. Deep learning methods have developed a lot in the field of computer vision which has provided a new target recognition method for remote sensing images. This paper concentrates on the application of convolutional neural networks(CNN) in object recognition of Unmanned Aerial Vehicle(UAV) remote sensing images: YOLOv3 and Faster R-CNN network model are built on computer and NVIDIA JETSON TX2, and NWPU VHR-10 dataset is used as training and test samples for simulation verification. The results show that the accuracy of remote sensing multiclass target recognition using the YOLOv3 network model is 73.8%, and the detection rate is 0.25s per image on average. The accuracy of using YOLOv3 network model in several network models is second only to Faster RCNN, but its real-time performance is better than one of Faster RCNN. The YOLOv3 network model is more appropriate for carrying on the UAVs.


  1. [1]  Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012), ImageNet Classification with Deep Convolutional Neural Networks, in Advances in Neural Information Processing Systems 25 (edited. by Pereira F, Burges C J C, Bottou L, et al.), Curran Associates, Inc., 1097-1105.
  2. [2]  Simonyan, K. and Zisserman, A. (2015), Very Deep Convolutional Networks for Large-Scale Image Recognition, 2015 International Conference on Learning Representations (ICRL).
  3. [3]  Szegedy, C., Liu, W., Jia, Y., et al (2015), Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  4. [4]  He, K., Zhang, X., Ren, S., et al (2016), Deep Residual Learning for Image Recognition: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  5. [5]  Girshick, R., Donahue, J., Darrell, T., et al. (2014), Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation: 2014 IEEE Conference on Computer Vision and Pattern Recognition.
  6. [6]  Girshick, R., (2015), Fast R-CNN: 2015 IEEE International Conference on Computer Vision (ICCV).
  7. [7]  Ren, S., He, K., Girshick, R., et al (2017), Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149.
  8. [8]  Dong, Z., Wang, M., Li, D., et al (2019), Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features, Acta Geodaetica et Cartographica Sinica, 48(10), 1285-1295.
  9. [9]  Liu, B., Wang, S., Zhao, J., et al (2019), Ship tracking and recognition based on Darknet network and YOLOv3 algorithm. Journal of Computer Applications, 39(06), 1663-1668
  10. [10]  Li, X., Chen, D., Liu, S., et al (2020), Tree species identification of multi-source remote sensing data based on improved 3D-CNN[J/OL], Laser & Optoelectronics Progress: 1-14. tn.20200601.0901.028.html.
  11. [11]  Redmon, J., Divvala, S., Girshick, R., et al (2016), You Only Look Once: Unified, Real-Time Object Detection: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  12. [12]  Redmon, J. and Farhadi, A. (2018), YOLOv3: An Incremental Improvement. arXiv e-prints, 1804-2767.
  13. [13]  Redmon, J. and Farhadi, A. (2017), YOLO9000: Better, Faster, Stronger: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  14. [14]  Cheng, G., Han, J., Zhou, P., et al (2014), Multi-class geospatial object detection and geographic image classification based on collection of part detectors, ISPRS Journal of Photogrammetry and Remote Sensing, 98, 119-132.
  15. [15]  Cheng, G. and Han, J. (2016), A survey on object detection in optical remote sensing images, ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
  16. [16]  Cheng, G., Zhou, P., and Han, J. (2016), Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7405-7415.
  17. [17]  Li, H., Li, C., An, J., et al (2019), Attention mechanism improves CNN remote sensing image object detection, Journal of Image and Graphics, 24(8), 1400-1408.
  18. [18]  Chen, D., Wan, G., and Li, K. (2019), Object detection in optical remote sensing images based on combination of multi-layer feature and context information, Acta Geodaetica et Cartographica Sinica, 48(10), 1275-1284.
  19. [19]  Wang, S., Wang, M., and Wang, G. (2019), Deep Neural Network Pruning Based Two-Stage Remote Sensing Image Object Detection. Journal of Northeastern University (Natural Science), 40(2), 174-179.