基于改进YOLOv5的智慧园区人车混杂 动态目标检测
DOI:
作者:
作者单位:

1.北京建筑大学;2.Beijing University of Civil Engineering and Architecture

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家重点研发项目(2018YFC0807806);北京建筑大学基本科研业务(X20109)。


Hybrid dynamic object detection of intelligent park based on improved YOLOv5
Author:
Affiliation:

1.北京建筑大学;2.Beijing University of Civil Engineering and Architecture

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    在城市安全背景下,人车密集的智慧园区安全问题日益引起关注。实时检测进入园区内的人员、车辆,可以为应急情景下制定园区内人车的合理疏散策略提供重要的数据参考。为解决当前目标检测在应急事件发生时的环境复杂、目标密集、遮挡等导致的精度问题,以及模型计算量导致的快速性问题,提出了一种基于改进轻量级YOLOv5的人车混杂目标检测算法。在原始YOLOv5模型的基础上,将骨干网中的空间金字塔池化结构SPPF改进为SimSPPF,保持并改善模型实时性。在中尺度层增加一条额外的边并引入通道注意力机制CA,使得模型在检测人车混杂的场景中的准确度得到提高。实验结果表明,相较于YOLOv5s,该算法在保持检测速度在142 FPS的同时,精度上提高了2.1个百分点,满足了智慧园区对于人、车混杂动态检测的准确性与实时性需求。

    Abstract:

    Under the background of urban security, the security problem of intelligent parks with dense human and vehicle has attracted increasing attention. Real-time detection of people and vehicles entering the park can provide important data reference for formulating reasonable evacuation strategies for people and vehicles in the park under emergency scenarios. In order to solve the accuracy problems caused by the complex environment, dense objects and occlusion of the current object detection during the occurrence of emergency events, as well as the rapidity problems caused by the amount of model calculation, a hybrid object detection algorithm based on improved lightweight YOLOv5 was proposed. Based on the original YOLOv5 model, the spatial pyramid pool structure SPPF in the backbone network is improved to SimSPPF to improve the real-time performance of the model. By adding an extra edge to the mesoscale layer and introducing the channel attention mechanism CA, the accuracy of the model in detecting the hybrid scene is improved. The experimental results show that compared with YOLOv5s, the algorithm not only keeps the detection speed at 142 FPS, but also improves the accuracy by 2.1 percentage points, which meets the accuracy and real-time requirements of the hybrid dynamic detection of people and vehicles in the smart park.

    参考文献
    相似文献
    引证文献
引用本文

胡玉玲,邹伟光,王鑫依. 基于改进YOLOv5的智慧园区人车混杂 动态目标检测[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-10-04
  • 最后修改日期:2024-05-15
  • 录用日期:2024-05-21
  • 在线发布日期:
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注