Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods
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arXiv
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| Format: | Preprint |
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2024
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| author | Elshaar, Mohssen E. Manaa, Zeyad M. Elbalshy, Mohammed R. Siddiqui, Abdul Jabbar Abdallah, Ayman M. |
| author_facet | Elshaar, Mohssen E. Manaa, Zeyad M. Elbalshy, Mohammed R. Siddiqui, Abdul Jabbar Abdallah, Ayman M. |
| contents | Unmanned Aerial Vehicles (UAVs) are becoming more popular in various sectors, offering many benefits, yet introducing significant challenges to privacy and safety. This paper investigates state-of-the-art solutions for detecting and tracking quadrotor UAVs to address these concerns. Cutting-edge deep learning models, specifically the YOLOv5 and YOLOv8 series, are evaluated for their performance in identifying UAVs accurately and quickly. Additionally, robust tracking systems, BoT-SORT and Byte Track, are integrated to ensure reliable monitoring even under challenging conditions. Our tests on the DUT dataset reveal that while YOLOv5 models generally outperform YOLOv8 in detection accuracy, the YOLOv8 models excel in recognizing less distinct objects, demonstrating their adaptability and advanced capabilities. Furthermore, BoT-SORT demonstrated superior performance over Byte Track, achieving higher IoU and lower center error in most cases, indicating more accurate and stable tracking.
Code: https://github.com/zmanaa/UAV_detection_and_tracking Tracking demo: https://drive.google.com/file/d/1pe6HC5kQrgTbA2QrjvMN-yjaZyWeAvDT/view?usp=sharing |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00285 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods Elshaar, Mohssen E. Manaa, Zeyad M. Elbalshy, Mohammed R. Siddiqui, Abdul Jabbar Abdallah, Ayman M. Computer Vision and Pattern Recognition Unmanned Aerial Vehicles (UAVs) are becoming more popular in various sectors, offering many benefits, yet introducing significant challenges to privacy and safety. This paper investigates state-of-the-art solutions for detecting and tracking quadrotor UAVs to address these concerns. Cutting-edge deep learning models, specifically the YOLOv5 and YOLOv8 series, are evaluated for their performance in identifying UAVs accurately and quickly. Additionally, robust tracking systems, BoT-SORT and Byte Track, are integrated to ensure reliable monitoring even under challenging conditions. Our tests on the DUT dataset reveal that while YOLOv5 models generally outperform YOLOv8 in detection accuracy, the YOLOv8 models excel in recognizing less distinct objects, demonstrating their adaptability and advanced capabilities. Furthermore, BoT-SORT demonstrated superior performance over Byte Track, achieving higher IoU and lower center error in most cases, indicating more accurate and stable tracking. Code: https://github.com/zmanaa/UAV_detection_and_tracking Tracking demo: https://drive.google.com/file/d/1pe6HC5kQrgTbA2QrjvMN-yjaZyWeAvDT/view?usp=sharing |
| title | Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.00285 |