Practical comparation of the accuracy and speed of YOLO, SSD and Faster RCNN for drone detection

Authors

  • Saad Mohammad Alkentar Al-Baath University Homs-Syria
  • B. Alsahwa HIAST Damascus-Syria
  • A. Assalem Al-Baath University Homs-Syria
  • D. Karakolla HIAST Damascus-Syria

DOI:

https://doi.org/10.31026/j.eng.2021.08.02

Keywords:

Faster RCNN, YOLO, SSD, Drone

Abstract

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN, YOLO, and SSD for effective drone detection in various environments. We have found that both Faster RCNN and YOLO have high recognition ability compared to SSD; on the other hand, SSD has good detection ability.

Downloads

Download data is not yet available.

Published

2021-08-01

How to Cite

Alkentar, S. M., Alsahwa, B., Assalem, A. and Karakolla, D. (2021) “Practical comparation of the accuracy and speed of YOLO, SSD and Faster RCNN for drone detection”, Journal of Engineering, 27(8), pp. 19–31. doi: 10.31026/j.eng.2021.08.02.