YOLO-PEL:基于YOLO算法的高效和轻量级车辆检测方法
Zhi Wang1, Kaiyu Zhang1, Fei Wu1
1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201602, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了YOLOv8-PEL,这是一种增强的车辆检测模型,可以提高固定摄像头系统的实时性能和效率. 它在减少计算资源的情况下实现了高精度,使其成为受限制应用的理想选择.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 车辆检测对于智能运输系统至关重要.
- 固定摄像头系统经常面临检测准确性,成本和实时性能之间的权衡.
- 现有的模型可能会在各个尺度上与特征融合和泛化作斗争.
研究的目的:
- 开发一种高效准确的车辆检测模型,用于资源有限的实时应用.
- 增强YOLOv8n模型,以提高功能适应性和多尺度集成.
- 为了应对极端样本导致的车辆检测精度挑战.
主要方法:
- 在YOLOv8n.引入了C2F-PPA模块,用于在YOLOv8n.中增强功能融合.
- 拟议的ELA-FPN用于精细的多尺度特征融合和泛化.
- 整合了Wise-IoUv3损失功能,以提高检测准确度.
- 在COCO-Vehicle和VisDrone2019数据集上训练和评估模型.
主要成果:
- 在COCO-车辆数据集上,YOLOv8-PEL实现了66.9%的mAP@0.5.
- 该模型拥有2.23M参数,7.0GFLOPs,4.5MB大小和176.8FPS的推断速度.
- 与YOLOv8n.相比,实现了参数 (25%),GFLOPs (13%),和模型大小 (25%) 的显著减少.
- 证明了卓越的计算效率和概括能力.
结论:
- YOLOv8-PEL提供了检测精度和计算效率的卓越平衡.
- 该模型非常适合实时和资源有限的车辆检测场景.
- 拟议的改进有效地改善了特征融合,并减轻了梯度问题,以便精确检测.
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