Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
Keywords:Surveillance, Deep Learning, Unmanned Aerial Vehicles, Object Detection, Computer vision, Image Processing
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures.
Bhaskaranand,M, a. J. (n.d.). Low-complexity video encoding for UAV reconnaissance and surveillance. Proc. IEEE Military Communications Conference (MILCOM), pp. 1633-1638, 2011.
Lim, a. S. Monocular Localization of a moving person onboard a Quadrotor MAV. Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 2182–2189, 2015.
I. Sa, S. H. Outdoor flight testing of a pole inspection UAV incorporating highspeed vision. Springer Tracts in Advanced Robotics, pp. 107–121, 2015.
Christos Kyrkou, G. P. DroNet: Efficient convolutional neural network detector for realtime UAV applications. Design, Automation and Test in Europe Conference and Exhibition (DATE), 2018.
J. Gokul Anand, “Trust based optimal routing in MANET’s,” 2011 International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoil, India, 2011, pp. 1150–1156, doi: 10.1109/ICETECT.2011.5760293.
S.Divyabharathi,“Large scale optimization to minimize network traffic using MapReduce in big data applications”. International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), pp. 193–199, April 2016. DOI: 10.1109/ICCPEIC.2016.7557196.
Paulraj, D 2020, ‘An Automated Exploring And Learning Model For Data Prediction Using Balanced CA-SVM’, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, no. 5, April 2020, DOI: https://doi.org/10.1007/s12652-020-01937-9
Berlin, M.A., Tripathi, S. et al. IoT-based traffic prediction and traffic signal control system for smart city. Soft Computing (2021). https://doi.org/10.1007/s00500-021-05896-x
P. Subbulakshmi, “Mitigating eavesdropping by using fuzzy based MDPOP-Q learning approach and multilevel Stackelberg game theoretic approach in wireless CRN”, Cognitive Systems Research, Vol. 52, pp. 853–861, 2018. doi: 10.1016/j.cogsys.2018.09.021.
V. D. Ambeth Kumar, S. Malathi, Abhishek Kumar, Prakash M and Kalyana C. Veluvolu, “Active Volume Control in Smart Phones Based on User Activity and Ambient Noise,” Sensors 2020, 20(15), 4117. doi: 10.3390/s20154117.
Sambit Satpathy, Sanchali Das, Swapan Debbarma, “A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA”, Journal of Supercomputing, vol. 76, no. 8, pp. 5849–5861, 2020. doi: 10.1007/s11227-019-03013-2.
Jangwon, L., Wang, J., Crandall, D., Šabanovic, S. and Fox, G., 2017. Real-time object detection for unmanned aerial vehicles based on cloud-based convolutional neural networks. Journal Concurrency and Computation: Practice and Experience, 29(6).
F. S. Leira, T. A. Johansen, and T. I. Fossen, “Automatic detection, classification and tracking of objects in the ocean surface from UAVs using a thermal camera,” in Proc. IEEE Aerospace Conference, pp. 1–10, 2015.
J. Engel, J. Sturm, and D. Cremers, “Scale-aware navigation of a low-cost quadrocopter with a monocular camera,” Robotics and Autonomous Systems, vol. 62, no. 11, pp. 1646–1656, 2014.
D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in neural information processing systems, pp. 2843–2851, 2012.
Alam, M.S., Natesha, B.V., Ashwin, T.S. and Guddeti, R.M.R., 2019. UAV based cost-effective real-time abnormal event detection using edge computing. Multimedia tools and Applications, 78(24), pp. 35119–35134.
Vaddi, S., 2019. Efficient object detection model for real-time UAV applications (Doctoral dissertation, Iowa State University).
Zhang, H., Liptrott, M., Bessis, N. and Cheng, J., 2019, September. Real-time traffic analysis using deep learning techniques and uav based video. In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1–5). IEEE.
Nguyen, A.Q., Nguyen, H.T., Tran, V.C., Pham, H.X. and Pestana, J., 2021, January. A Visual Real-time Fire Detection using Single Shot MultiBox Detector for UAV-based Fire Surveillance. In 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE) (pp. 338–343). IEEE.
Lai, Y.C. and Huang, Z.Y., 2020. Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation. Remote Sensing, 12(18), p. 3035.
Dong, J., Ota, K. and Dong, M., 2021. UAV-based Real-Time Survivor Detection System in Post-disaster Search and Rescue Operations. IEEE Journal on Miniaturization for Air and Space Systems.
Redmon, J., and Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517–6525.
Loey, M., Manogaran, G., Taha, M.H.N. and Khalifa, N.E.M., 2021. Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-152 for medical face mask detection. Sustainable cities and society, 65, p. 102600.
Lu, J., Huang, J. and Lu, F., 2019. Distributed kernel extreme learning machines for aircraft engine failure diagnostics. Applied Sciences, 9(8), p. 1707.
Ding, G., Dong, F. and Zou, H., 2019. Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding. Applied Soft Computing, 84, p. 105704.
Sun, C., Zhan, W., She, J. and Zhang, Y., 2020. Object detection from the video taken by drone via convolutional neural networks. Mathematical Problems in Engineering, 2020.