CFI-Former:通过多颗粒度感知查询注意力转换器有效检测车道
Rong Gao1, Siqi Hu2, Lingyu Yan2
1School of Computer Science, Hubei University of Technology, Wuhan, 430068, China; State Key Laboratory for Novel Software Technology at Nanjing University, Nanjing, 210023, China.
概括
CFI-Former通过使用多细分感知查询注意力来改进特征细节并减少冗余信息来改善车道检测. 一种新的加权适应性损失在具有挑战性的场景中提高了性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 变压器方法具有先进的车道检测性能.
- 现有的方法在查询序列中遭受重复和无效信息的影响,偏向本地化特征处理.
- 不准确的车道线形状限制妨碍了精确的检测.
研究的目的:
- 提出CFI-Former,一种基于变压器的新车道检测方法.
- 通过处理冗余信息和改进特征细节提取来提高车道检测准确度.
- 在具有挑战性的车道检测场景中提高稳健性.
主要方法:
- 引入了一个多细分感知查询注意 (GQA) 模块,用于提取详细的车道信息.
- 实施了两阶段的查询过程 (从粗到细),以过不相关的信息.
- 开发了一个加权的自适应交叉与联盟 (IoU) 损失 (Lφ-IoU),以提高在困难情况下的性能.
主要成果:
- 该GQA模块有效地从全球到本地提取多颗粒度车道特征.
- 两个阶段的查询有效地过冗余数据,将注意力集中在相关地区.
- 权重自适应IOU损失可自适应地调整高IOU对象的梯度,提高具有挑战性的场景性能.
结论:
- 与基线方法相比,CFI-Former实现了更准确的车道检测.
- 拟议的GQA模块和加权自适应IOU损失有助于改进车道检测能力.
- 在基准车道检测数据集上,CFI-Former表现出卓越的性能.
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