FRCP-YOLO:路上物体检测算法基于改进的YOLOv8n
Dongmei Liu1, Changchun Wang1, Xuejun Li1
1School of Electronic Information Engineering, Changchun University, Changchun, Jilin, China.
PloS one
|February 20, 2026
概括
FRCP-YOLO模型通过提高道路物体检测准确性和稳定性来提高自动驾驶汽车的安全性. 它以更少的参数实现更高的检测性能,解决复杂驾驶场景中的挑战.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 准确的道路物体检测对于自动驾驶汽车的安全至关重要.
- 目前的模型面临的挑战是小物体,低精度和低强度.
研究的目的:
- 提出FRCP-YOLO,这是一个基于YOLOv8n.n.的增强道路物体检测模型.
- 为了提高检测准确性,减少模型复杂性,增强强性,特别是对于小物体.
主要方法:
- 用FasterNet Block替换了C2f模块,以更快地提取功能.
- 引入了R-CA模块,用于改进对象焦点和特征学习.
- 实现了用于小物体检测的高分辨率分支和检测头.
- 利用PIoU v2损失函数进行精确的边界框回归.
主要成果:
- 在KITTI数据集上,FRCP-YOLO在KITTI数据集上实现了0.924 mAP@50和0.667 mAP@50-95,比基线的表现分别高出5.0%和6.6%.
- 与基线相比,模型参数减少了4%.
- 在BDD100K数据集上在复杂的场景中表现出卓越的性能,如密集的交通和弱光.
结论:
- FRCP-YOLO为道路物体检测提供了更高的准确性,效率和稳定性.
- 该模型显示出强大的概括能力,使其可靠在各种条件下进行自动驾驶.
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