| Autor: |
Gupta, Priyanka, Pareek, Bhavya, Singal, Gaurav, Rao, D. Vijay |
| Předmět: |
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| Zdroj: |
Multimedia Tools & Applications; Jun2022, Vol. 81 Issue 14, p19813-19834, 22p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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