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在恶劣天气条件下,基于PP-LiteSeg网络的改进点云消噪方法
1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.
PeerJ. Computer science
|March 4, 2024
概括
这项研究引入了一种新的系列注意力聚变无线网络 (SAFDN),以改善自动驾驶在恶劣天气中的LiDAR点云无线化. SAFDN增强了特征提取和注意力机制,显著提高了实时拒绝的准确性和速度.
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
- 传感器数据处理 传感器数据处理
背景情况:
- 立达点云数据 (PCD) 对自动驾驶感知至关重要.
- 恶劣天气会降低LiDAR PCD质量,影响探测范围并引入噪音.
- 现有的拒绝算法在具有挑战性的条件下难以获得准确性和速度.
研究的目的:
- 为恶劣天气条件提出实时点云消除网络 (SAFDN).
- 为了提高特征提取和细分精度,对于杂的LiDAR数据.
- 提高自动驾驶感知系统的整体性能.
主要方法:
- 引入了具有扩展卷积的WeatherBlock模块,用于增强受体场和多尺度特征提取.
- 开发了使用顺序通道和空间注意力机制的系列注意力融合模块 (SAFM).
- 实施加权特征融合,以整合低级和高级特征信息.
主要成果:
- 与基线PP-Lite.Seg相比,SAFDN模型显示无效精度 (MIOU) 增加了11.1%.
- 实现了205.06 FPS的高推断速度,使实时处理成为可能.
- 在模拟恶劣天气中显著提高了噪声识别准确度和无噪声能力.
结论:
- 拟议的SAFDN有效地解决了在恶劣天气下现有的点云消噪方法的局限性.
- SAFDN提供了一个强大的解决方案来提高LiDAR数据质量,这对于安全的自动驾驶至关重要.
- 该模型的实时性能使其适合在自主系统中实际部署.
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