基于改进的YOLOv5s的轻量级车辆检测
Yuhai Wang1, Shuobo Xu1, Peng Wang1
1School of Information and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
本研究引入了一种新的轻量级YOLOv5车辆检测算法,使用集成感知注意 (IPA) 和多尺度空间通道重建 (MSCCR) 模块. 改进的算法减少了9%的参数,同时增加了3.1%的检测精度.
科学领域:
- 计算机视觉和人工智能的人工智能
- 智能运输系统 智能运输系统
背景情况:
- 车辆检测算法对于智能交通管理至关重要.
- 现有的算法经常面临准确性和计算成本之间的权衡.
研究的目的:
- 提出一个轻量级但准确的车辆检测算法.
- 增强YOLOv5算法,以改善交通管理系统.
主要方法:
- 开发了一种轻量级的集成感知注意 (IPA) 模块,配备了变压器编码器.
- 引入了一个多尺度空间通道重建 (MSCCR) 模块,用于高效的特征学习.
- 将IPA和MSCCR集成到YOLOv5s的骨干网络中.
主要成果:
- 与原来的YOLOv5.5相比,模型参数减少了约9%.
- 平均精度 (mAP@50) 提高了3.1%.
- 保持计算复杂度 (FLOPS) 没有增加.
结论:
- 拟议的方法有效地减少了模型参数,并提高了检测精度.
- 这种轻量级的YOLOv5改进适用于智能交通管理应用程序.
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