通过数据合并和YOLOv8进行自动驾驶的恶劣天气中对象检测
Debasis Kumar1, Naveed Muhammad1
1Institute of Computer Science, University of Tartu, Narva Maantee 18, 51009 Tartu, Estonia.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
在恶劣天气中提高自动驾驶感知是至关重要的. 这项研究使用合并数据集和转移学习来增强YOLOv8对象检测,在具有挑战性的条件下显著提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶在很大程度上依赖于对环境的感知,而这种感知在恶劣天气条件下会显著降低.
- 动态物体和环境变化对感知系统构成固有的挑战.
- 恶劣的天气 (雪,雨,雾等) 进一步损害传感器数据质量,影响安全性和可靠性.
研究的目的:
- 为了提高基于摄像头的自动驾驶系统的感知精度,特别专注于在恶劣天气下对象检测.
- 通过合并数据集研究转移学习的有效性,以提高YOLOv8在恶劣条件下的性能.
主要方法:
- 使用了YOLOv8对象检测模型.
- 员工通过对合并数据集 (ACDC和DAWN) 及其子集的模型进行训练来转移学习.
- 从单个和合并的数据集收集和比较训练权重.
- 评估了各种恶劣天气数据集和子集的检测性能.
主要成果:
- 与标准YOLOv8基础权重相比,针对定制的,合并的数据集的训练显著提高了对象检测性能.
- 通过与特征相关的数据合并来增加训练数据的数量,始终提高了对象检测的准确性.
- 拟议的方法在恶劣天气条件下检测主要道路物体方面表现出卓越的性能.
结论:
- 使用合并的恶劣天气数据集进行转移学习是改进基于YOLOv8的对象检测的有效策略.
- 数据合并技术和利用更大,多样化的数据集是实现自动驾驶强大的感知系统的关键.
- 这些发现有助于开发更可靠的自动驾驶感知系统,能够在具有挑战性的环境条件下运行.
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