Now-a-days, UAVs such as drones are greatly used for various purposes like that of capturing and target detection from ariel imagery etc. Easy access of these small ariel vehicles to public can cause serious security threats. For instance, critical places may be monitored by spies blended in public using drones. Study in hand proposes an improved and efficient Deep Learning based autonomous system which can detect and track very small drones with great precision. The proposed system consists of a custom deep learning model ‘Tiny YOLOv3’, one of the flavors of very fast object detection model ‘You Look Only Once’ (YOLO) is built and used for detection. The object detection algorithm will efficiently the detect the drones. The proposed architecture has shown significantly better performance as compared to the previous YOLO version. The improvement is observed in the terms of resource usage and time complexity. The performance is measured using the metrics of recall and precision that are 93% and 91% respectively.
[1]
Tauseef Jamal,et al.
Malware Classification Using Deep Boosted Learning
,
2021,
ArXiv.
[2]
Quoc V. Le,et al.
Searching for Activation Functions
,
2018,
arXiv.
[3]
Asifullah Khan,et al.
Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
,
2021,
Applied Sciences.
[4]
Sujata Chaudhari,et al.
Yolo Real Time Object Detection
,
2020
.
[5]
Ali Farhadi,et al.
You Only Look Once: Unified, Real-Time Object Detection
,
2015,
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6]
Guigang Zhang,et al.
Deep Learning
,
2016,
Int. J. Semantic Comput..
[7]
Wendong Gai,et al.
An improved Tiny YOLOv3 for real-time object detection
,
2021,
Systems Science & Control Engineering.
[8]
Siddharth Swarup Rautaray,et al.
Deep Learning Approaches for Detecting Objects from Images: A Review
,
2018
.