A compact solution for ultra-light drone optical auto-detection and distance estimation using AI
151 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.83.2022.11-21Keywords:
Ultra-Light Drones; Black Dot; YOLOv3 Model; Drone detection; Verification.Abstract
This paper proposes a system for ultra-light drone (ULD) auto–detection using only one non-static optical PTZ camera. The system includes multi-stages of suspect objects detection, clarification, and distance estimation. An AI model for detection and clarification stages is designed based on the YOLOv3 architecture and trained with a practical dataset. In the detection stage, the camera continuously pans, tilts, and zooms to take panoramic images of the detection zone and pass them to the AI model. Once the AI model detects a suspect object, it will switch to the verification stage. In this stage, the camera controlled by the AI model’s output focuses on the target to clarify and estimate the distance to ULD. The proposed solution was implemented and tested with popular fly cams. The results show that the system can auto-detect ultra-light drones effectively with high accuracy.
References
[1]. Y. C. Lai, Z. Y. Huang, “Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation,” Remote Sens. (2020). https://doi.org/10.3390/rs12183035. DOI: https://doi.org/10.3390/rs12183035
[2]. F. Svanström, C. Englund and F. Alonso-Fernandez, "Real-Time Drone Detection and Tracking with Visible, Thermal and Acoustic Sensors," 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7265-7272, (2021), doi: 10.1109/ICPR48806.2021.9413241. DOI: https://doi.org/10.1109/ICPR48806.2021.9413241
[3]. E. Unlu, E. Zenou, N. Riviere, P. E. Dupouy, “Deep learning-based strategies for the detection and tracking of drones using several cameras,” IPSJ T Comput Vis Appl 11, 7 (2019). https://doi.org/10.1186/s41074-019-0059-x. DOI: https://doi.org/10.1186/s41074-019-0059-x
[4]. Igor S. Golyak, Dmitriy R. Anfimov, Iliya S. Golyak, Andrey N. Morozov, Anastasiya S. Tabalina, and Igor L. Fufurin, “Methods for real-time optical location and tracking of unmanned aerial vehicles using digital neural networks,” Proc. SPIE 11394, Automatic Target Recognition XXX, 113941B (2020); doi: 10.1117/12.2573209. DOI: https://doi.org/10.1117/12.2573209
[5]. N. H. Hoang, N. L. Cuong, T. V. Kien, “Measuring the arrival time of signal to determine coordinates of ultra-light drone,” Journal of Military Science and Technology, FEE (2020), (in Vietnamese).
[6]. Seidaliyeva, Ulzhalgas & Akhmetov, Daryn & Ilipbayeva, Lyazzat & Matson, Eric., “Real-Time and Accurate Drone Detection in a Video with a Static Background,” Sensors. 20. 3856. 10.3390/s20143856, (2020). DOI: https://doi.org/10.3390/s20143856
[7]. Y. Hu, X. Wu, G. Zheng and X. Liu, "Object Detection of UAV for Anti-UAV Based on Improved YOLO v3," 2019 Chinese Control Conference (CCC), 2019, pp. 8386-8390, (2019). doi: 10.23919/ChiCC.2019.8865525. DOI: https://doi.org/10.23919/ChiCC.2019.8865525
[8]. D. K. Behera and A. Bazil Raj, "Drone Detection and Classification using Deep Learning," 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1012-1016, (2020). doi: 10.1109/ICICCS48265.2020.9121150. DOI: https://doi.org/10.1109/ICICCS48265.2020.9121150
[9]. Hassan, Syed & Rahim, Tariq & Shin, Soo., “Real-time UAV Detection based on Deep Learning Network,” 630-632. 10.1109/ICTC46691.2019.8939564, (2019). DOI: https://doi.org/10.1109/ICTC46691.2019.8939564
[10]. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” arXiv:1506.02640v5 [cs.CV], (2016). DOI: https://doi.org/10.1109/CVPR.2016.91
[11]. Joseph Redmon, Ali Farhadi, “YOLOv3: An Incremental Improvement,” arXiv: 1804.02767v1 [cs.CV], (2018).
[12]. S.V. Viraktamath, M. Yavagal, R. Byahatti, “Object Detection and Classification using YOLOv3”, International Journal of Engineering Research & Technology, Vol. 10, Issue 02, (2021).
