分析YOLOx在恶劣天气条件下检测车辆的性能.
Imran Ashraf1, Soojung Hur1, Gunzung Kim2
1Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
约洛克斯 (YOLOx) 模型在恶劣天气条件 (如雪,雾,雨和沙暴) 中检测车辆方面表现出强的表现. 图像增强技术进一步提高其准确性,用于自动驾驶汽车的感知.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 自主系统 自主系统
背景情况:
- 自动驾驶汽车的发展在很大程度上依赖于准确的道路车辆检测.
- 具有挑战性的环境条件 (雨,雾,雪,沙暴) 显著阻碍了车辆检测的准确性.
- 实时性能对于自动驾驶汽车的安全性和功能性至关重要.
研究的目的:
- 调查YOLOx物体检测模型在恶劣天气条件下识别车辆的有效性.
- 在不同的数据集上评估不同的YOLOx变体 (YOLOx-s,YOLOx-m,YOLOx-l).
- 探索图像增强对YOLOx在恶劣天气中的性能的影响.
主要方法:
- 使用了YOLOx对象检测模型,特别是它的's', 'm'和'l'变体.
- 在DAWN基准数据集上测试该模型,该数据集包括各种恶劣天气场景的图像.
- 使用精度,回忆和平均平均精度 (mAP) 评估模型性能.
- 进行了多尺度视网膜图像增强的实验.
主要成果:
- 与YOLOx-m和YOLOx-l相比,YOLOx-s变种实现了更高的性能.
- 在所有测试的天气条件中,YOLOx-s显示了高的mAP分数:0.8983 (雪),0.8656 (沙暴),0.9509 (雨) 和0.9524 (雾).
- 与雨和沙尘暴相比,在雪和雾中表现明显更好.
- 多尺度视网膜图像增强对YOLOx的性能产生了积极的影响.
结论:
- YOLOx-s是一个能够在恶劣天气条件下实时检测车辆的模型.
- 图像质量提升是一种可行的策略,可以进一步提高在具有挑战性的环境中检测的准确性.
- 这些发现有助于推进自动驾驶汽车的强大的感知系统.
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