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A UAV aided lightweight target information collection and detection approach

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Abstract

With the resumption of the World Cup and various concerts, the number of heavily crowded scenarios grows intensely. In these cases, it poses new challenges to massive information collection and lightweight target detection. Fortunately, the booming development of unmanned aerial vehicle (UAV) technology provides a highly flexible and cost-effective solution in many scenarios. This paper proposes a UAV aided lightweight target information collection and detection approach, where the target information is carried to a terrestrial distributed platform by a UAV and then a fast target detection is implemented. Firstly, we implement a 3D trajectory optimization for the UAV by minimizing the information collection time. Secondly, we design a lightweight target detection algorithm based on UAV loadable ARM (Advanced RISC Machines) architecture edge computing device. Finally, a terrestrial distributed processing platform is established. To ensure the stability and reliability of the target detection system, each module is tested separately. Numerical simulations show that, with the target detection module deployed on the Jetson Xavier NX edge computing platform for testing, the proposed target detection approach can achieve a detection accuracy of 89.5\(\%\) and 71FPS detection speed using GPU acceleration compared with state-of-the-art methods.

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References

  1. Chohan UW, Van Kerckhoven S (2023) Activist retail investors and the future of financial markets: understanding YOLO capitalism. Taylor & Francis, ???

  2. Chen K, Li H, Li C, Zhao X, Wu S, Duan Y, Wang J (2022) An automatic defect detection system for petrochemical pipeline based on cycle-gan and yolo v5. Sensors 22(20):7907

    Article  Google Scholar 

  3. Zeng Y, Zhang R (2017) Energy-efficient uav communication with trajectory optimization. IEEE Trans Wirel Commun 16(6):3747–3760

    Article  Google Scholar 

  4. Zhu H, Qi Y, Shi H, Li N, Zhou H (2018) Human detection under uav: an improved faster r-cnn approach. In: 2018 5th International conference on systems and informatics (ICSAI), pp 367–372. IEEE

  5. Shen Y, Zhu Y, Kang H, Sun X, Chen Q, Wang D (2021) Uav path planning based on multi-stage constraint optimization. Drones 5(4):144

    Article  Google Scholar 

  6. Liu Y, Zhang X, Guan X, Delahaye D (2016) Potential odor intensity grid based uav path planning algorithm with particle swarm optimization approach. Math Probl Eng 2016

  7. Huang C, Lan Y, Liu Y, Zhou W, Pei H, Yang L, Cheng Y, Hao Y, Peng Y (2018) A new dynamic path planning approach for unmanned aerial vehicles. Complexity 2018:1–17

    Article  Google Scholar 

  8. Zhou X, Gao F, Fang X, Lan Z (2021) Improved bat algorithm for uav path planning in three-dimensional space. IEEE Access 9:20100–20116

    Article  Google Scholar 

  9. Ruan W-y, Duan H-b (2020) Multi-uav obstacle avoidance control via multi-objective social learning pigeon-inspired optimization. Front Inf Technol Electron 21(5):740–748

    Article  Google Scholar 

  10. Blondel P, Potelle A, Pégard C, Lozano R (2014) Human detection in uncluttered environments: from ground to uav view. In: 2014 13th International conference on control automation robotics & vision (ICARCV), pp 76–81. IEEE

  11. Feng Y, Yu S, Peng H, Li Y-R, Zhang J (2021) Detect faces efficiently: a survey and evaluations. IEEE Trans Biom Behav Identity Sci 4(1):1–18

    Article  Google Scholar 

  12. Liu Y, Wang F, Deng J, Zhou Z, Sun B, Li H (2022) Mogface: Towards a deeper appreciation on face detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4093–4102

  13. He Y, Xu D, Wu L, Jian M, Xiang S, Pan C (2019) Lffd: a light and fast face detector for edge devices. arXiv:1904.10633

  14. Zhang S, Zhu X, Lei Z, Shi H, Wang X, Li SZ (2017) S3fd: Single shot scale-invariant face detector. In: Proceedings of the IEEE international conference on computer vision, pp 192–201

  15. Chi C, Zhang S, Xing J, Lei Z, Li SZ, Zou X (2019) Selective refinement network for high performance face detection. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8231–8238

  16. Liu Y, Tang X, Han J, Liu J, Rui D, Wu X (2020) Hambox: Delving into mining high-quality anchors on face detection. In: 2020 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 13043–13051 . IEEE

  17. Bazarevsky V, Kartynnik Y, Vakunov A, Raveendran K, Grundmann M (2019) Blazeface: sub-millisecond neural face detection on mobile gpus. arXiv:1907.05047

  18. Qi D, Tan W, Yao Q, Liu J (2023) Yolo5face: Why reinventing a face detector. In: Computer vision–ECCV 2022 workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part V, Springer, pp 228–244

  19. Kim N, Kim J-H, Won CS (2022) Fafd: fast and accurate face detector. Electronics 11(6):875

    Article  Google Scholar 

  20. Wu W, Peng H, Yu S (2023) Yunet: a tiny millisecond-level face detector. Mach Intell Res 1–10

  21. Sun R, Matolak DW (2016) Air-ground channel characterization for unmanned aircraft systems part ii: hilly and mountainous settings. IEEE Trans Veh Technol 66(3):1913–1925

    Article  Google Scholar 

  22. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  23. Zhu Q, Yeh M-C, Cheng K-T, Avidan S (2006) Fast human detection using a cascade of histograms of oriented gradients. In: 2006 IEEE Computer society conference on computer vision and pattern recognition (CVPR’06), vol. 2, pp 1491–1498. IEEE

  24. Yang B, Yan J, Lei Z, Li SZ (2014) Aggregate channel features for multi-view face detection. In: IEEE International joint conference on biometrics, pp 1–8. IEEE

  25. Brubaker SC, Wu J, Sun J, Mullin MD, Rehg JM (2008) On the design of cascades of boosted ensembles for face detection. Int J Comput Vis 77:65–86

    Article  Google Scholar 

  26. Pham M-T, Cham T-J (2007) Fast training and selection of haar features using statistics in boosting-based face detection. In: 2007 IEEE 11th International conference on computer vision, pp 1–7. IEEE

  27. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Neural Inf Process Syst 28

  28. Jiang H, Learned-Miller E (2017) Face detection with the faster r-cnn. In: 2017 12th IEEE International conference on automatic face & gesture recognition (FG 2017), pp 650–657. IEEE

  29. Deng J, Guo J, Ververas E, Kotsia I, Zafeiriou S (2020) Retinaface: single-shot multi-level face localisation in the wild. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5203–5212

  30. Tang X, Du DK, He Z, Liu J (2018) Pyramidbox: a context-assisted single shot face detector. In: Proceedings of the european conference on computer vision (ECCV), pp 797–813

  31. Li J, Wang Y, Wang C, Tai Y, Qian J, Yang J, Wang C, Li J, Huang F (2019) Dsfd: dual shot face detector. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5060–5069

  32. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. Preprint arXiv:1704.04861

  33. Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1314–1324

  34. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  35. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  36. Najibi M, Samangouei P, Chellappa R, Davis LS (2017) Ssh: single stage headless face detector. In: Proceedings of the IEEE international conference on computer vision, pp 4875–4884

  37. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

  38. Zheng Z, Wang P, Ren D, Liu W, Ye R, Hu Q, Zuo W (2021) Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans Cybern 52(8):8574–8586

    Article  Google Scholar 

  39. Khan A, Aftab F, Zhang Z (2019) Bicsf: Bio-inspired clustering scheme for fanets. IEEE Access 7:31446–31456

    Article  Google Scholar 

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Acknowledgements

The National Natural Science Foundation of China is funding JianShan Su under Grant No. 62165015. The Autonomous Region Graduate Student Innovation Program is funding Meng Huang under Grant No. XJ2023G258

Funding

National Natural Science Foundation of China, 62165015. Autonomous Region Graduate Student Innovation Program, XJ2023G258.

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Contributions

Conceptualization, H.L. and H.Z.; methodology, J.S. and M.H.; software, T.M.; validation, J.S., Y.Z. and T.M.; formal analysis, M.H.; investigation, H.Z.; resources, J.S.; data curation, Y.Z.; writing–original draft preparation, M.H.; writing–review and editing, H.Z.; visualization, M.H.; supervision, J.S.; project administration, J.S.; funding acquisition, J.S.

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Correspondence to Jinshan Su or Haibo Zhou.

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Huang, M., Li, H., Zhou, Y. et al. A UAV aided lightweight target information collection and detection approach. Peer-to-Peer Netw. Appl. 17, 1667–1681 (2024). https://doi.org/10.1007/s12083-024-01659-1

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