Sec-CLOCs:多模式后端基于融合的在雪景中的物体检测算法
Rui Gong1, Xiangsuo Fan1,2, Dengsheng Cai3
1School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究介绍了Sec-CLOCs,这是一种用于在重雪中进行强大的车辆检测的多式融合方法. 它增强了传感器数据,并结合了2D和3D检测,提高了驾驶安全.
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
- 传感器融合式传感器
背景情况:
- 像大雪这样的恶劣天气条件显著降低了智能驾驶汽车中的LiDAR和相机性能.
- 目前的环境传感能力不足以在恶劣天气下安全操作,这对驾驶安全构成风险.
研究的目的:
- 提出和评估Sec-CLOCs,这是一种多式联网后端聚变物体检测方法,优化用于在暴雪中检测车辆.
- 提高自动驾驶汽车在恶劣天气条件下的环境传感能力.
主要方法:
- 改进的YOLOv8s 2D探测器与使用后端融合方法的第二个3D探测器的集成.
- 通过双阶段知识学习和多对比规范化 (TKLMR) 算法进行图像数据增强.
- 使用LIDROR算法进行3D检测的点云数据预处理,然后通过CLOCs融合2D和3D结果.
主要成果:
- 在中等条件下 (30-100米) Sec-CLOCs实现了82.34%的车辆检测准确度,在暴雪期间在恶劣条件下 (>100米) 达到81.76%.
- 该方法在恶劣的雪环境中显示出高的检测性能和稳定性.
- 优化包括用于YOLOv8s的DyHead检测头和Wise-IOU丢失功能,以及用于图像质量的TKLMR.
结论:
- 该Sec-CLOCs算法显著提高了在暴雪条件下车辆检测性能.
- 增强的2D和3D传感器数据的多模式后端融合为恶劣天气中自动驾驶安全提供了强大的解决方案.
- 拟议的方法提高了智能汽车环境传感的可靠性.
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