1
Civil Engineering Dept., Imam Hussein university
2
Imam Hussein Comprehensive University
10.22059/eoge.2025.389458.1167
Abstract
A micro-unmanned aerial vehicle (micro-UAV) is a type of unmanned aerial vehicle characterized by compact dimensions, typically spanning a few centimeters, and is capable of autonomous operation. Micro-UAVs are employed in a diverse range of applications, thereby presenting potential security threats. Consequently, the initial step in mitigating these threats involves the accurate and rapid detection of micro-UAVs. The advent of artificial intelligence (AI) technology has significantly enhanced the detection and efficiency of micro-UAVs. Among the most prominent AI algorithms for detecting micro-UAVs in the visible spectrum are YOLOv8 and Faster R-CNN. This study aims to compare the efficiency of YOLOv8 and Faster R-CNN, focusing on their trade-offs between detection accuracy and processing speed for micro-UAV surveillance. To this end, the performance of YOLOv8 and Faster R-CNN has been evaluated in terms of detection accuracy and processing speed. The dataset utilized comprises a comprehensive collection of 3,492 images gathered by micro-UAVs during environmental monitoring operations, categorized randomly into three distinct subsets: 70% for training, 20% for validation, and 10% for testing. Experimental results indicate that the YOLOv8 algorithm achieves a true detection rate of approximately 98.6% in detecting micro-UAVs, whereas the Faster R-CNN algorithm attains a true detection rate of approximately 99.6%. Furthermore, YOLOv8 requires an average of 0.03 seconds to process each frame, whereas Faster R-CNN necessitates 2.5 seconds. The comparative analysis reveals that the YOLOv8 algorithm is more suitable for real-time applications and surveillance systems that necessitate rapid image processing due to its significantly higher speed. Conversely, the Faster R-CNN algorithm is a preferable choice for applications where high accuracy is the primary priority, as it offers superior detection accuracy despite requiring more processing time.
khazai, S. and Mirzaei, S. (2024). A Comparative Analysis of YOLO and Faster R-CNN Algorithms for Micro-UAVs Detection in Surveillance Videos. Earth Observation and Geomatics Engineering, 8(1), -. doi: 10.22059/eoge.2025.389458.1167
MLA
khazai, S. , and Mirzaei, S. . "A Comparative Analysis of YOLO and Faster R-CNN Algorithms for Micro-UAVs Detection in Surveillance Videos", Earth Observation and Geomatics Engineering, 8, 1, 2024, -. doi: 10.22059/eoge.2025.389458.1167
HARVARD
khazai, S., Mirzaei, S. (2024). 'A Comparative Analysis of YOLO and Faster R-CNN Algorithms for Micro-UAVs Detection in Surveillance Videos', Earth Observation and Geomatics Engineering, 8(1), pp. -. doi: 10.22059/eoge.2025.389458.1167
CHICAGO
S. khazai and S. Mirzaei, "A Comparative Analysis of YOLO and Faster R-CNN Algorithms for Micro-UAVs Detection in Surveillance Videos," Earth Observation and Geomatics Engineering, 8 1 (2024): -, doi: 10.22059/eoge.2025.389458.1167
VANCOUVER
khazai, S., Mirzaei, S. A Comparative Analysis of YOLO and Faster R-CNN Algorithms for Micro-UAVs Detection in Surveillance Videos. Earth Observation and Geomatics Engineering, 2024; 8(1): -. doi: 10.22059/eoge.2025.389458.1167
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