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Edge device based Military Vehicle Detection and Classification from UAV

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Abstract

Detection and recognition of military vehicle from a given image or a video frame with the help of unmanned aircraft system is the major issue which we are concerned about. Vehicle identification and classification from a resource constraint device embedded on an aerial vehicle integrated with an intelligent object detection algorithm, is a big support for defence agency. The vehicle can be controlled both manually and autonomously. However, there is no military objects/vehicles dataset openly available with different varieties of military classes. Hence, we propose our dataset having 6772 images with classes namely, Military Trucks, Military Tanks, Military Aircrafts, Military Helicopters, Civilian Car and Civilian aircraft. Quantize SSD Mobilenet v2 and Tiny Yolo v3 deep learning models are trained on our dataset and compared its performance over resources constraint edge devices. The observations and results from the research show that Tiny Yolo v3 performs well over the other model and is highly efficient and can even run with edge based devices due to it’s light weight. There is a detailed generalised mathematical calculation provided which calculates the number of flight paths and the total number of frames required to cover a given area for surveillance using available hardware specification. This work will be suitable for classifying the military and civilian vehicle in the real time scenario using edge device over UAVs.

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Notes

  1. Gupta, Priyanka; Pareek, Bhavya; Singal, Gaurav; Rao, D Vijay (2021), ”Military and Civilian Vehicles Classification”, Mendeley Data, V1, https://doi.org/10.17632/njdjkbxdpn.1

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Acknowledgements

Special thanks to Archika Shukla, Hitesh Goyal, Manthan Jain, Nishant Katara, Reeya Jain, Utkarsh Pancholi, Vipul Maheshwari and Yash Garg from LNMIIT, Jaipur for helping in the annotation of the dataset.

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Author notes

    Authors and Affiliations

    1. JECRC Foundation Jaipur, Jaipur, India

      Priyanka Gupta

    2. The LNM Institute of Information Technology, Jaipur, India

      Bhavya Pareek

    3. Netaji Subhas University of Technology, Delhi, India

      Gaurav Singal

    4. Defence Research and Development Organisation, Metcalfe House Delhi, India

      D. Vijay Rao

    Authors
    1. Priyanka Gupta
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    2. Bhavya Pareek
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    3. Gaurav Singal
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    4. D. Vijay Rao
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    Corresponding author

    Correspondence to Gaurav Singal.

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    P. Gupta and B. Pareek contributed equally to this paper

    Appendix

    Appendix

    • UAV - Unmanned Aerial Vehicle

    • UAS - Unmanned Aircraft System

    • SSD - Single-shot Detector

    • YOLO - You Only Look Once

    • CNN - Convolutional Neural Networks

    • R-CNN - Region Based Convolutional Neural Networks

    • SVM - Support Vector Machine

    • GPU - Graphics Processing Unit

    • HOG - Histogram of Oriented Gradient

    • KCF - Kernelized Correlation Filter

    • RGBD - Red, Green, Blue, Depth

    • RGB - Red, Green, Blue

    • AUC - Area under the ROC Curve

    • VOC - Pascal Visual Object Classes

    • TPU - TensorFlow Processing Unit

    • ISRO - Indian Space Research Organisation

    • jpeg - Joint Photographic Experts Group

    • jpg - Joint Photographic Experts

    • png - Portable Network Graphics

    • txt - Text

    • xml - Extensible Markup Language

    • csv - Comma Separated Values

    • TF - TensorFlow

    • ReLU - Rectified Linear Unit

    • RAM - Random Access Memory

    • CUDA - Compute Unified Device Architecture

    • cuDNN - CUDA Deep Neural Network

    • GPIO - General-Purpose Input/Output

    • USB - Universal Serial Bus

    • GPS - Global Positioning System

    • PWM - Pulse Width Modulation

    • ESC - Electronic Speed Controller

    • RC -

    • MAVLink - Micro Air Vehicle Link

    • mAP - Mean Average Precision

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    Gupta, P., Pareek, B., Singal, G. et al. Edge device based Military Vehicle Detection and Classification from UAV. Multimed Tools Appl 81, 19813–19834 (2022). https://doi.org/10.1007/s11042-021-11242-y

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    • DOI: https://doi.org/10.1007/s11042-021-11242-y

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