ZZ-YOLOv11:一个基于改进的YOLOv11的轻量级车辆检测模型
Zhe Zhang1, Zhongyang Zhang1, Gang Li1
1School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究介绍了ZZ-YOLO,这是一种改进的轻量级网络,用于车辆检测,提高准确性和减少城市交通中的计算负载. 新模型显著减少了错误检测和遗漏,改善了现实世界的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 城市交通场景给车辆检测带来了挑战,原因是复杂的照明和堵塞.
- 现有的检测算法在准确性,高错误率和计算需求方面扎.
- 需要更高效,更准确的轻型车辆检测网络.
研究的目的:
- 提出一个改进的轻量级车辆检测网络ZZ-YOLO,解决准确性和计算负担.
- 在检测模型中增强对象边缘焦点和特征融合.
- 为了减少模型参数,同时保持高检测性能.
主要方法:
- 开发了一个全球边缘信息传输 (GEIT) 模块,以改进功能表示.
- 引入了一种轻量级的细节卷积检测头 (LDCD),用于参数缩小和特征融合.
- 应用了基于层适应的大小的修剪 (LAMP) 和模型蒸 (YOLOv11x + LDCD作为教师).
主要成果:
- 在KITTI和BDD100K数据集上,ZZ-YOLO实现了70.9%的检测准确度和58%的mAP@0.5.
- 与原始算法相比,检测精度增加了5.7%,平均精度增加了2.3%.
- 减少了34%的模型参数,并在真实车辆测试中有效降低了错误检测和遗漏率.
结论:
- 拟议的ZZ-YOLO网络为城市交通轻型车辆检测提供了显著的改进.
- 集成GEIT和LDCD模块,以及修剪和蒸,提高了准确性和效率.
- ZZ-YOLO有效地解决了当前方法的局限性,显示了对现实世界交通监控应用的希望.
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