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Mustafa Fahem Al Baghdadi
University of Babylon
Electrical & Electronics Engineering

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Unmanned aerial vehicles and machine learning for detecting objects in real time Mustafa Fahem Al Baghdadi; Mehdi Ebady Manaa
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4185

Abstract

An unmanned aerial vehicle (UAV) image recognition system in real-time is proposed in this study. To begin, the you only look once (YOLO) detector has been retrained to better recognize objects in UAV photographs. The trained YOLO detector makes a trade-off between speed and precision in object recognition and localization to account for four typical moving entities caught by UAVs (cars, buses, trucks, and people). An additional 1500 UAV photographs captured by the embedded UAV camera are fed into the YOLO, which uses those probabilities to estimate the bounding box for the entire image. When it comes to object detection, the YOLO competes with other deep-learning frameworks such as the faster region convolutional neural network. The proposed system is tested on a wild test set of 1500 UAV photographs with graphics processing unit GPU acceleration, proving that it can distinguish objects in UAV images effectively and consistently in real-time at a detection speed of 60 frames per second.
Co-Authors Mehdi Ebady Manaa