在恶劣天气条件下强大的物体检测:用于汽车视觉系统的ECL-YOLOv11
Zhaohui Liu1, Jiaxu Zhang1, Xiaojun Zhang1
1College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了ECL-YOLOv11,这是一个用于自动驾驶的增强物体检测框架. 它通过整合边缘增强,上下文引导的多尺度融合和轻量级检测头来提高恶劣天气中的准确性和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 智能交通系统和自动驾驶在很大程度上依赖于视觉感知.
- 恶劣的天气条件降低了图像质量,影响了对象检测的准确性和交通安全.
- 传统的检测模型在具有挑战性的环境中难以减少对比度,模糊和噪声.
研究的目的:
- 提出一个增强的物体检测框架,ECL-YOLOv11,在恶劣天气下提供强大的性能.
- 为了提高车载感知系统的检测精度和实时处理.
- 为在具有挑战性的条件下提供可靠的自动驾驶安全解决方案.
主要方法:
- 开发了ECL-YOLOv11,集成了三个模块:卷积边缘增强 (CE),上下文引导的多尺度融合网络 (AENet) 和轻量级共享卷积检测头 (LDHead).
- CE模块结合了边缘和卷积特征,以在低可见度的情况下保留边界信息.
- 通过多尺度融合和上下文建模,AENet增强了小型/远距离对象的感知.
- 通过共享卷积和GroupNorm进行实时推断,LDHead优化了效率.
主要成果:
- ECL-YOLOv11实现了62.7%的mAP@50和40.5%的mAP@50-95,比基线YOLOv11的表现分别高出1.3%和0.8%.
- 精度达到了73.1%的处理速度为237.8 FPS,证明了精度和速度之间的平衡.
- 废弃性研究证实了单个模块及其协同集成的有效性.
- 定性结果显示,在各种不利条件下,高可靠性检测和减少错误.
结论:
- ECL-YOLOv11为自动驾驶中的全天候感知提供了可靠和适应性的基础.
- 该框架通过提高对象检测的稳定性来确保运营安全和实时响应.
- 集成模块有效地解决了特征退化,多尺度挑战和计算效率的问题.
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