YOLO-FDCL:改进了YOLOv8,用于在复杂的照明条件下检测驾驶员疲劳
Genchao Liu1, Kun Wu2, Wei Lan2
1School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究介绍了YOLO-FDCL,这是一种用于检测复杂照明下驾驶员疲劳的新算法. 它显著提高了检测准确度和在充满挑战的驾驶条件下的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路交通安全 道路交通安全
背景情况:
- 驾驶员疲劳是道路交通事故的主要原因之一.
- 车中复杂的照明条件降低了现有的疲劳检测系统的准确性.
- 准确的疲劳识别对于提高道路交通安全至关重要.
研究的目的:
- 开发一种新的算法,YOLO-FDCL,用于在复杂的照明条件下强大的驾驶员疲劳检测.
- 提高疲劳检测系统在充满挑战的驾驶环境中的准确性和性能.
- 为了解决当前方法在车内的不同灯光场景中的局限性.
主要方法:
- 将MobileNetV4引入骨干网络,以增强功能提取和减少模型大小.
- 集成RepFPN,在部进行结构重新参数化,以改善多尺度特征融合.
- 在YAWDD数据集和自主开发的复杂照明驾驶疲劳数据集 (CLDFD) 上评估了算法.
主要成果:
- 在YAWDD数据集上,与YOLOv8-S相比,YOLO-FDCL的精度提高到98.8%,回忆率提高到97.5%.
- 在YAWDD数据集上实现了更高的mAP@0.5 (98.8%) 和mAP@0.5:0.95 (94.2%).
- 在CLDFD上表现出显著的性能增长,精度和回忆分别增加了2.8%和2.2%.
结论:
- YOLO-FDCL在驾驶员疲劳检测方面取得了重大进展,特别是在复杂的照明条件下.
- 该算法增强了特征提取和多尺度融合,从而实现了卓越的检测性能.
- 这项工作通过提供更准确和可靠的方法来识别驾驶员疲劳,有助于提高道路安全.
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