Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지
DOI QR코드

DOI QR Code

Indoor Environment Drone Detection through DBSCAN and Deep Learning

  • Ha Tran Thi (Dept. of Smart Information and Technology Engineering, Kongju National University) ;
  • Hien Pham The (Dept. of Smart Information and Technology Engineering, Kongju National University) ;
  • Yun-Seok Mun (Dept. of Smart Information and Technology Engineering, Kongju National University) ;
  • Ic-Pyo Hong (Dept. of Smart Information and Technology Engineering, Kongju National University)
  • Received : 2023.11.01
  • Accepted : 2023.12.05
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

Keywords

Acknowledgement

This work was supported in part by the Basic Science Research Program under Grant 2020R1I1A3057142, and in part by the Underground City of the Future Program funded by the Ministry of Science and ICT.

References

  1. Ahirwar, S and Swarnkar, R and Bhukya, S and Namwade, G, "Application of drone in agriculture," International Journal of Current Microbiology and Applied Sciences, vol.8, no.1, pp.2500-2505, 2019. DOI: 10.20546/ijcmas.2019.801.264 
  2. Son, Seung Woo and Yu, Jae Jin and Kim, Dong Woo and Park, Hyun Su and Yoon, Jeong Ho, "Applications of drones for environmental monitoring of pollutant-emitting facilities," Proceedings of NIE, vol.2, no.4, pp.298-304, 2021. DOI: 10.22920/PNIE.2021.2.4.298 
  3. Fan, Jin and Saadeghvaziri, M Ala, "Applications of drones in infrastructures: Challenges and opportunities," International Journal of Mechanical and Mechatronics Engineering, vol.13, no.10, pp.649-655, 2019. DOI: 10.5281/zenodo.3566281 
  4. Bjorklund, S, "Target detection and classification of small drones by boosting on radar micro-Doppler," in Proc. of 2018 15th European Radar Conference (EuRAD), pp.182-185, 2018. DOI: 10.23919/EuRAD.2018.8546569 
  5. Jian, Michael and Lu, Zhenzhong and Chen, Victor C, "Drone detection and tracking based on phase-interferometric Doppler radar," in Proc. of 2018 IEEE Radar Conference (RadarConf18), pp.1146-1149, 2018. DOI: 10.1109/RADAR.2018.8378723 
  6. Park, J., Jung, D. H., Bae, K. B., & Park, S. O, "Range-Doppler map improvement in FMCW radar for small moving drone detection using the stationary point concentration technique," IEEE Transactions on Microwave Theory and Techniques, vol.68, no.5, pp.1858-1871, 2020. DOI: 10.1109/TMTT.2019.2961911 
  7. Companik, E., Gravier, M. J., & Farris II, M. T, "Feasibility of warehouse drone adoption and implementation," Journal of Transportation Management, vol.28, no.2, pp.5, 2018. DOI: 10.22237/jotm/1541030640 
  8. Wawrla, L., Maghazei, O., & Netland, T, "Applications of drones in warehouse operations," Whitepaper. ETH Zurich, D-MTEC, pp.212, 2019. 
  9. Ester, Martin, et al, "A density-based algorithm for discovering clusters in large spatial databases with noise," kdd, vol.96, no. 34, pp.226-231, 1996. 
  10. He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian, "Deep residual learning for image recognition," in Proc. of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. DOI: 10.1109/CVPR.2016.90 
  11. Hochreiter, Sepp, and Jurgen Schmidhuber, "Long short-term memory," Neural computation, vol.9, no.8, pp.1735-1780, 1997. DOI: 10.1162/neco.1997.9.8.1735