一个目标检测模型HR-YOLO用于在雾条件下先进的驾驶辅助系统
1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China.
Scientific reports
|April 16, 2025
概括
在雾条件下,HR-YOLO在高级驾驶辅助系统 (ADAS) 中增强了车辆和行人检测. 这种改进的YOLO模型通过使用新的脱雾和注意力模块来提高准确性和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 雾条件显著降低了高级驾驶辅助系统 (ADAS) 中对象检测算法的性能.
- 现有的检测模型在降低可见性方面扎,影响车辆和行人实时准确度.
研究的目的:
- 开发一个改进的基于YOLO的模型,HR-YOLO,用于准确和实时的车辆和行人检测,特别是在雾条件下.
- 为了增强特征提取,图像质量,特征融合效率,并在恶劣天气中提高目标定位精度.
主要方法:
- 引入高效高精度除雾网络 (EHPD-Net) 以加强特征提取.
- 集成增强全球空间注意力 (EGSA) 模块,用于改进小目标检测.
- 应用深度规范化除雾网络 (DND-Net) 用于提高图像质量.
- 集成动态样本 (DySample) 模块和优化卷积/C2f模块用于特征融合.
- 利用了智能交叉对联 (WIoU) 损失函数,以提高本地化准确度.
主要成果:
- HR-YOLO实现了5.9%的改善,在RTTS雾数据集上达到79.8%的mAP.
- 在雾城市景观数据集中,HR-YOLO显示了9.7%的改善,达到49.5%的mAP.
- 该模型显示,与基线模型相比,准确性和实时性能显著提高.
结论:
- HR-YOLO提供了一种有效的解决方案,用于在雾的环境中强大的目标检测.
- 拟议的模型在具有挑战性的气象条件下提高了ADAS的能力.
- 这项工作为未来在恶劣天气下自主系统感知方面的研究奠定了基础.
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