可变形的金字塔形变压器用于半监督的驾驶员分心检测
Qiang Zhao1, Zhichao Yu2, Jiahui Yu2
1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种适应性的半监督框架,用于检测驾驶员的分心,通过使用有限的标记数据和未标记的样本来显著改善安全系统,以获得准确的性能.
科学领域:
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 司机的注意力对于智能运输安全至关重要.
- 对于驾驶员分心检测模型的手动注释是昂贵和耗时的.
- 现有的模型在有限的标记数据中扎.
研究的目的:
- 提出一个适应性的半监督驾驶员分心检测框架.
- 用有限的标记数据提高模型性能.
- 增强现实世界的驾驶员监控系统.
主要方法:
- 使用教师-学生学习和可变形金字塔特征融合.
- 包含一个自适应的伪标签优化策略,具有类别意识的门和信任权重.
- 在YOLOv11探测器中集成一个可变形金字塔散子变压器 (DPST) 模块.
- 使用教师指导的特征一致性蒸.
主要成果:
- 使用有限的标记数据和大量的未标记数据实现了强大的和可扩展的分心检测.
- DPST模块增强了对微妙驾驶员行为的细粒度感知.
- 在mAP指标中,在Roboflow分心驾驶数据集上表现优于完全监督的基线.
- 在精度和回忆之间展示了一个平衡的权衡.
结论:
- 拟议的框架为在有限的注释条件下检测驾驶员分心提供了有效的解决方案.
- 能够实现实用和可扩展的驾驶员监控系统.
- 通过特征一致性蒸减轻噪音伪标签的影响.
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