Design of object detection and weak position location system base on coaxial dual-rotor drone

doi:10.7523/j.ucas.2022.017

Abstract

Abstract: Unmanned aerial vehicles (UAV) controlled by autonomous control algorithms often have greater advantages than manned aircraft in the execution of directional strikes and other dangerous tasks. When performing blasting tasks, the robustness of the UAV detection algorithm in different scenarios is often not guaranteed, which greatly affects the UAV's positioning of the target, thus reducing the success rate of the mission. In order to solve the above problems, the crossdomain-based object detection algorithm is used to improve the robustness of the UAV detection algorithm in different scenarios, and the online GPS clustering algorithm is used to improve the robustness of object positioning. At the same time, in view of the impact of the object blasting position on the blasting result, the system uses an algorithm for locating weak parts to improve the accuracy and success rate of blasting.

Key words: unmanned aerial vehicles, object detection, knowledge distillation, image segmentation

CLC Number:

TP319.4

FENG Hangtao, ZENG Shaofeng, ZHANG Lu, YANG Xu, LIU Zhiyong. Design of object detection and weak position location system base on coaxial dual-rotor drone[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(5): 701-709.

References

[1] Zhao J H, Xiao G, Zhang X C, et al. A survey on object tracking in aerial surveillance[C]//Proceedings of International Conference on Aerospace System Science and Engineering 2018, 2019:53-68. DOI:10.1007/978-981-13-6061-9_4.
[2] Vanegas F, Campbell D, Roy N, et al. UAV tracking and following a ground target under motion and localisation uncertainty[C]//2017 IEEE Aerospace Conference. March 4-11, 2017, Big Sky, MT, USA. IEEE, 2017:1-10. DOI:10.1109/AERO.2017.7943775.
[3] Tijtgat N, Van Ranst W, Volckaert B, et al. Embedded real-time object detection for a UAV warning system[C]//2017 IEEE International Conference on Computer Vision Workshops. October 22-29, 2017, Venice, Italy. IEEE, 2017:2110-2118. DOI:10.1109/ICCVW.2017.247.
[4] Du D W, Qi Y K, Yu H Y, et al. The unmanned aerial vehicle benchmark:Object detection and tracking[M]//Computer Vision-ECCV 2018. Cham:Springer International Publishing, 2018:375-391. DOI:10.1007/978-3-030-01249-6_23.
[5] Mittal P, Singh R, Sharma A. Deep learning-based object detection in low-altitude UAV datasets:a survey[J]. Image and Vision Computing, 2020, 104:104046. DOI:10.1016/j.imavis.2020.104046.
[6] Zou Z X, Shi Z W, Guo Y H, et al. Object detection in 20 years:A survey[EB/OL]. arXiv:1905.05055. (2019-05-16)[2021-11-01]. https://arxiv.org/abs/1905.05055v1.
[7] Parmar N, Vaswani A, Uszkoreit J, et al. Image transformer[EB/OL]. arXiv:1802.05751. (2018-02-15)[2021-11-01]. https://arxiv.org/abs/1802.05751.
[8] Park T, Efros A A, Zhang R, et al. Contrastive learning for unpaired image-to-image translation[C]//Computer Vision-ECCV 2020, 2020:319-345. DOI:10.1007/978-3-030-58545-7_19.
[9] Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations[EB/OL]. arXiv:2002.05709. (2020-02-13)[2021-11-01]. https://arxiv.org/abs/2002.05709.
[10] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. June 23-28, 2014, Columbus, OH, USA. IEEE, 2014:580-587. DOI:10.1109/CVPR.2014.81.
[11] Girshick R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. December 7-13, 2015, Santiago, Chile. IEEE, 2015:1440-1448. DOI:10.1109/ICCV.2015.169.
[12] Ren S Q, He K M, Girshick R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149. DOI:10.1109/TPAMI.2016.2577031.
[13] Redmon J, Divvala S, Girshick R, et al. You only look once:unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016:779-788. DOI:10.1109/CVPR.2016.91.
[14] Redmon J, Farhadi A. YOLO9000:better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017:6517-6525. DOI:10.1109/CVPR.2017.690.
[15] Redmon J, Farhadi A. YOLOv3:an incremental improvement[EB/OL]. arXiv:1804.02767. (2018-04-08)[2021-11-01]. https://arxiv.org/abs/1804.02767v1.
[16] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4:optimal speed and accuracy of object detection[EB/OL]. arXiv:2004.10934. (2020-04-23)[2021-11-01]. https://arxiv.org/abs/2004.10934v1.
[17] Liu W, Anguelov D, Erhan D, et al. SSD:single shot MultiBox detector[C]//Computer Vision-ECCV 2016, 2016:21-37. DOI:10.1007/978-3-319-46448-0_2.
[18] Duan K W, Bai S, Xie L X, et al. CenterNet:keypoint triplets for object detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019, Seoul, Korea (South). IEEE, 2019:6568-6577. DOI:10.1109/ICCV.2019.00667.
[19] Hinton G E, Vinyals O, Dean J. Distilling the knowledge in a neural network[J]. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2015, abs/1503.02531.
[20] McKinley G A. An introduction to digital image processing[J]. Clinical Microbiology Newsletter, 1990, 12(12):89-92. DOI:10.1016/0196-4399(90)90015-4.
[21] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66. DOI:10.1109/TSMC.1979.4310076.
[22] Sauvola J, Pietikäinen M. Adaptive document image binarization[J]. Pattern Recognition, 2000, 33(2):225-236. DOI:10.1016/S0031-3203(99)00055-2.