边缘CaseDNet:一种增强的检测架构,用于在自动驾驶中感知边缘情况
Yin Lei1, Chen Shan1, Wancheng Ge1
1College of Electronic and Information Engineering, Tongji University, Shanghai, China.
PloS one
|December 18, 2025
概括
本研究介绍了EdgeCaseDNet,这是一种用于自动驾驶的先进物体检测框架,可以在具有挑战性的边缘情况下提高可靠性,例如恶劣天气和多尺度目标.
科学领域:
- 计算机视觉 计算机视觉
- 自主驾驶系统 自主驾驶系统
- 机器学习 机器学习
背景情况:
- 自动驾驶感知系统在极端情况下扎,例如恶劣的天气和多个规模的目标.
- 在具有挑战性的环境条件下,检测可靠性受到损害.
研究的目的:
- 为自动驾驶边缘案例开发一个优化的对象检测框架.
- 引入一个新的边缘案例数据集,对极端道路条件进行精确的注释.
主要方法:
- 开发了EdgeCaseDNet,扩展了YOLOv8与Haar_HGNetv2骨干,非对称特征金字塔网络,混合部分深度可分离卷积和Wise-IoU损失.
- 创建了一个新的边缘案例数据集,包括雾,雨,雪和夜间条件.
主要成果:
- 在全面的评估中,EdgeCaseDNet在YOLOv8上表现出优势.
- 实现了 +10.6% 的 mAP@50 和 +8.4% 的 mAP@[.5:.95] 改进.
结论:
- 在自动驾驶边缘情况下,EdgeCaseDNet显著提高了对象检测可靠性.
- 拟议的框架和数据集有助于为自动驾驶汽车提供更强大的感知系统.
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