道路表面的语义细分用于自动驾驶的自动驾驶
Huaqi Zhao1, Su Wang1, Xiang Peng1
1The Heilongjiang Provincial Key Laboratory of Autonomous Intelligence and Information Processing, School of Information and Electronic Technology, Jiamusi University, Jiamusi, Heilongjiang, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种基于频率的语义细分与变压器 (FSSFormer),以改善复杂的自动驾驶场景中的道路表面细分,提高重叠目标和道路边界的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 自主驾驶系统 自主驾驶系统
背景情况:
- 语义细分对于自动驾驶至关重要,但与复杂的道路环境作斗争.
- 现有的方法在准确划分路面时往往存在局限性,特别是重叠的物体和边界细节.
研究的目的:
- 提出一种新的基于频率的语义细分方法,使用变压器架构 (FSSFormer).
- 在具有挑战性的交通条件下提高道路表面细分的性能.
- 改进对重叠目标和边界信息丢失的处理.
主要方法:
- 开发了一个基于频率的语义细分与变压器 (FSSFormer) 模型.
- 引入了一个重量分担的因子化注意力机制来选择关键频率特征.
- 采用交叉注意力方法,将空间和频率特征结合起来,以精确边界细节.
- 使用并行通道的前送网络进行位置信息编码.
主要成果:
- 拟议的FSSFormer在复杂的道路场景中展示了改进的细分性能.
- 与Cityscapes数据集上的现有方法相比,在整个欧盟 (mIoU) 中平均交叉点增加了2%.
- 成功解决了与重叠目标和边界信息丢失相关的挑战.
结论:
- FSSFormer为自动驾驶的语义细分提供了显著的进步.
- 基于频率的方法有效地利用频率信息来增强道路表面细分.
- 该方法显示了现实世界自动驾驶应用程序的巨大潜力,这些应用程序需要高细分精度.
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