DV3-IBi_YOLOv5s:一个轻量级的骨干网络和多层次的子网络车辆检测算法.
Liu Wang1,2,3, Lijuan Shi1,2,4, Jian Zhao1,2,3
1Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Ministry of Education, Changchun University, Changchun 130022, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究引入了一种新的车辆检测算法,为智能运输平衡速度和准确性. 拟议的方法实现了高检测准确度和速度,适合移动设备.
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:
- 车辆检测对于智能交通系统,自动驾驶和城市规划至关重要.
- 现有的方法往往难以平衡轻量网络的速度和多尺度网络的准确性.
研究的目的:
- 开发一种车辆检测算法,将轻量级网络的速度与多尺度网络的准确性相结合.
- 为了提高各种尺寸和类别的车辆的特征处理和检测精度.
主要方法:
- 利用MobileNetV3作为一个轻量级的骨干网络,以提高检测速度.
- 整合了ICAM注意力机制以改进特征处理.
- 在子网络中集成BiFPN和ICAM模块,以改进多尺度检测.
主要成果:
- 在Ua-Detrac数据集上实现了71.19%的检测准确度.
- 该算法的参数数量为3.8 MB.
- 证明了每秒120.02 (fps) 的检测速度.
结论:
- 拟议的算法有效地平衡了车辆检测的准确性和速度.
- 结果符合移动设备上嵌入式系统在参数数量,速度和准确性方面的要求.
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