Abstract
The arbitrary use of drones poses great threat to public safety and personal privacy. It is necessary to detect the intruding drones in sensitive areas in real time. In this paper, we design a real-time drone detector using deep learning approach. Specifically, we improve a well-performed deep learning model, i.e., You Only Look Once, by modifying its structure and tuning its parameters to better accommodate drone detection. Considering that a robust detector needs to be trained using a large amount of training images, we also propose a semi-automatically dataset labelling method based on Kernelized Correlation Filters tracker to speed up the pre-processing of the training images. At last, the performance of our detector is verified via extensive experiments.
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References
Wargo, C., Snipes, C., Roy, A., Kerczewski, R.: UAS industry growth: forecasting impact on regional infrastructure, environment, and economy. In: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), pp. 1–5. IEEE (2016)
Chang, X., Yang, C., Wu, J., Shi, X., Shi, Z.: A surveillance system for drone localization and tracking using acoustic arrays. In: 2018 IEEE 87th Vehicular Technology Conference (2018)
Chang, X., Yang, C., Shi, X., Li, P., Shi, Z., Chen, J.: Feature extracted DOA estimation algorithm using acoustic array for drone surveillance. In: 2018 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (2018)
Yang, C., Wu, Z., Chang, X., Shi, X., Wo, J., Shi, Z.: DOA estimation using amateur drones harmonic acoustic signals. In: 2018 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (2018)
Shi, X., Yang, C., Xie, W., Liang, C., Shi, Z., Chen, J.: Anti-drone system with multiple surveillance technologies: architecture, implementation, and challenges. IEEE Commun. Mag. 56(4), 68–74 (2017)
Chen, J., Kang, H., Wang, Q., Sun, Y., Shi, Z., He, S.: Narrowband internet of things: implementations and applications. IEEE Internet Things J. 4(6), 2309–2314 (2017)
Sevil, H.E., Dogan, A., Subbarao, K., Huff, B.: Evaluation of extant computer vision techniques for detecting intruder sUAS. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 929–938. IEEE (2017)
Hwang, S., Lee, J., Shin, H., Cho, S., Shim, D.H.: Aircraft detection using deep convolutional neural network in small unmanned aircraft systems. In: 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, p. 2137 (2018)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing, vol. 1, pp. 582–585. IEEE (1994)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893. IEEE Computer Society (2005)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.J.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587. IEEE Computer Society (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788. IEEE Computer Society (2016)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Usc drone dataset. https://chelicynly.github.io/Drone-Project/
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR, pp. 6517–6525. IEEE Computer Society (2017)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA, pp. 1027–1035. SIAM (2007)
Acknowledgments
This work was supported by NSFC under Grant 61772467, Zhejiang Provincial Natural Science Foundation of China under Grant LR16F010002, 973 Project under Grant 2015CB352503, the Fundamental Research Funds for the Central Universities (2017XZZX009-01), and China Postdoctoral Science Foundation funded project.
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Wu, M., Xie, W., Shi, X., Shao, P., Shi, Z. (2018). Real-Time Drone Detection Using Deep Learning Approach. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_3
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DOI: https://doi.org/10.1007/978-3-030-00557-3_3
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