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雪钟:相机-激光雷达对象候选融合,用于在雪天条件下进行3D对象检测
Xiangsuo Fan1,2, Dachuan Xiao1, Qi Li1,3
1School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
由于传感器数据的扭曲,在雪中检测车辆具有挑战性. 新的多模式算法Snow-CLOCs通过增强的图像和LiDAR处理,改善了在恶劣天气下3D物体的检测.
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:
- 现有的3D物体检测因传感器数据扭曲 (LiDAR,摄像头) 而与恶劣的天气,特别是雪相斗争.
- 单传感器方法和当前的多模式方法面临着数据扭曲和对齐准确性的挑战,阻碍了可靠的对象检测.
研究的目的:
- 开发一个强大的多式联运物体检测算法,Snow-CLOCs,专门设计用于具有挑战性的雪天条件.
- 增强特征提取并减少基于图像的检测中的数据质量依赖性.
- 通过减少噪声来提高 LiDAR 点云处理的精度.
主要方法:
- 增强了YOLOv5与InceptionNeXt用于图像特征提取和Wise-IoU以减轻数据质量问题.
- 改进了用于LiDAR检测的SECOND算法,包括DROR过器来消除噪音.
- 摄像机和LiDAR检测结果的融合使用稀疏张量和2D卷积神经网络来实现统一的对象检测和定位.
主要成果:
- 在雪天下,Snow-CLOCs在物体检测准确度方面显著提高.
- 在雪的环境中实现了86.61%的车辆检测准确度.
- 在恶劣天气中成功解决了与数据扭曲和低对齐精度相关的挑战.
结论:
- 拟议的Snow-CLOCs算法为在恶劣的雪天气中对3D物体检测提供了强大的解决方案.
- 多模式融合,结合对图像和LiDAR数据的特定增强,有效地克服了环境挑战.
- 这种方法显著提高了在恶劣气象条件下运行的自主系统的可靠性.
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