Unmanned aerial vehicle (UAV) object detection using YOLO detector
Nowadays, unmanned aerial vehicles (UAVs) have gained significant popularity i n computer vision (CV) and remote sensing (RS). Concurrently, GPU acceleration has facilitated the widespread adoption of deep l earning (DL). This dissertation comprehensively reviews mainstream object detection metho...
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| 其他作者: | |
| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/175475 |
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| 總結: | Nowadays, unmanned aerial vehicles (UAVs) have gained significant popularity i n
computer vision (CV) and remote sensing (RS). Concurrently, GPU acceleration has
facilitated the widespread adoption of deep l earning (DL). This dissertation
comprehensively reviews mainstream object detection methods and relevant literature.
YOLOv8, one of the SOTA (State Of The Art) detectors based on the deep learning
framework, is implemented to fulfill the UAV based object detection task. Meanwhile,
several representative one stage and two stage detectors are also tested for comparison .
The effectiveness of setting different input image sizes and batch sizes for training is
also evaluated and discussed. Inspired by experiments conducted in this dissertation
and related literature, the baseline of YOLOv8 is modified and the modified version
achieves 2% ~ 5% higher accuracy. |
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