Multi-Modal Pedestrian Detection Algorithm Based on Deep Learning

doi:10.7652/xjtuxb202210006

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Multi-Modal Pedestrian Detection Algorithm Based on Deep Learning

更新时间：2025-07-09

- Multi-Modal Pedestrian Detection Algorithm Based on Deep Learning
- Vol. 56, Issue 10, Pages: 61-70(2022)
- 作者机构：
  
  1. 西安工业大学电子信息工程学院,西安,710021
  2. 西安翔迅科技有限责任公司,西安,710068
  3. 西安工业大学发展规划处,西安,710021
  4. 陕西航天技术应用研究院有限公司,西安,710100
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202210006
  CLC： TP391.4
- Online First：10 October 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
Multi-Modal Pedestrian Detection Algorithm Based on Deep Learning[J]. 2022, 56(10): 61-70.
DOI：

Multi-Modal Pedestrian Detection Algorithm Based on Deep Learning[J]. 2022, 56(10): 61-70. DOI： 10.7652/xjtuxb202210006.

摘要

针对全天候工作的多模态行人检测算法体积大、运算量高、效率不足的问题

提出一种基于深度学习MBNet算法搭建的轻量级多模态行人检测算法(G-MBNet)。采用ResNet18算法并结合跨阶段链接的思想搭建CSP-ResNet18轻量级特征提取网络

以保证检测算法精度; 引入轻量级高效通道注意力(ECA)模块来提升特征提取网络对重要特征的关注能力

在引入极少参数的情况下提升算法的检测精度; 通过引入轻量级Ghost卷积模块来重构MBNet算法的特征提取网络

在保证特征提取性能的情况下进一步降低算法的参数与体积

提升算法的检测速度。采用所提的G-MBNet算法在KAIST行人数据集进行测试

实验结果表明:G-MBNet算法大小是原始算法的32.33%

参数量是原始算法的37.81%

检测速度是原始算法的1.53倍; G-MBNet算法可在保证行人识别精度的情况下有效提升检测速度。

Abstract

This paper proposes a lightweight multi-modal pedestrian detection algorithm based on deep learning MBNet algorithm to address the problem of 24/7 multi-modal pedestrian detection algorithm having a large size

high computation load and insufficient efficiency. First

the CSP-ResNet18 lightweight feature extraction network is built using the ResNet18 algorithm combined with the idea of cross-stage linking to ensure accuracy of the detection algorithm; then

the lightweight efficient channel attention module is introduced to enhance the feature extraction network's ability to focus on important features

which can improve the detection accuracy of the algorithm by introducing very few parameters; finally

the feature extraction network of MBNet algorithm is reconstructed by introducing a lightweight Ghost convolution module

which can further reduce the parameters required and the size of the algorithm and improve its detection efficiency while ensuring the feature extraction performance. The proposed G-MBNet algorithm is tested on the KAIST pedestrian dataset

and the experimental results show that the size of G-MBNet algorithm is 32.33% that of the original algorithm; the number of parameters is 37.81% that of the original algorithm; the detection speed is 1.53 times that of the original algorithm. The experiments verify that G-MBNet algorithm can effectively improve the detection speed while ensuring the pedestrian recognition accuracy.

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references

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