[1]李伟达,叶靓玲,郑力新,等.面向扶梯不安全行为的改进型深度学习检测算法[J].华侨大学学报(自然科学版),2022,43(1):119-126.[doi:10.11830/ISSN.1000-5013.202105059]
　LI Weida,YE Liangling,ZHENG Lixin,et al.Improved Deep Learning Detection Algorithm for Unsafe Escalator Behavior[J].Journal of Huaqiao University(Natural Science),2022,43(1):119-126.[doi:10.11830/ISSN.1000-5013.202105059]
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面向扶梯不安全行为的改进型深度学习检测算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第43卷
期数:
2022年第1期
页码:
119-126
栏目:
出版日期:
2022-01-09

文章信息/Info

Title:
Improved Deep Learning Detection Algorithm for Unsafe Escalator Behavior
文章编号:
1000-5013(2022)01-0119-08
作者:
李伟达1; 2;  叶靓玲1; 2;  郑力新1; 2;  朱建清1; 2;  曾远跃3;  林俊杰3
1. 华侨大学 工学院, 福建 泉州 362021;2. 华侨大学 工业智能化与系统福建省高校工程研究中心, 福建 泉州 362021;3. 福建省特种设备检验研究院 泉州分院, 福建 泉州 362021
Author(s):
LI Weida1; 2;  YE Liangling1; 2;  ZHENG Lixin1; 2; ZHU Jianqing1; 2;  ZHENG Yuanyue3;  LIN Junjie3
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. Industrial Intelligence and System Fujian University Engineering Research Center, Huaqiao University, Quanzhou 362021, China; 3. Quanzhou Branch of Special Equipment Inspection Research Institute, Quanzhou 362021, China
关键词:
扶梯;  不安全行为;  目标检测;  YOLOv5s;  CBAM模块;  Ghost卷积模块
Keywords:
escalator;  unsafe behavior;  object detection;  YOLOv5s;  CBAM module;  Ghost convolution module
分类号:
TP391.41;TU229
DOI:
10.11830/ISSN.1000-5013.202105059
文献标志码:
A
摘要:
以YOLOv5s网络模型为基础,引入注意力机制CBAM模块,基于Ghost卷积模块重构网络模型的卷积操作,提出一种面向扶梯不安全行为的改进型深度学习检测算法.然后,在自主收集的扶梯不安全行为数据集上对其进行训练评估.结果表明,所提算法在检测精度有所提高的同时,大幅减少了检测所需的参数量和计算量.
Abstract:
An improved deep learning detection algorithm for unsafe escalator behavior was proposed. The algorithm is based on the YOLOv5s network model, introduces the attention mechanism CBAM module, and reconstructs the convolution operation of the network model based on the Ghost convolution module. It is trained and evaluated on the self-collected escalator unsafe behavior data set. The results show that the proposed algorithm has improved the detection accuracy while greatly reducing the amount of parameters and calculation required for detection.

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备注/Memo

备注/Memo:
收稿日期: 2021-05-23
通信作者: 郑力新(1967-),男,教授,博士,主要从事光电检测与智能计算的研究.E-mail:zlxgxy@hqu.edu.cn.
基金项目: 国家自然科学基金面上资助项目(61976098); 福建省泉州市高层次人才创新创业项目(2020C042R); 福建省科技计划项目(2020Y0039)
更新日期/Last Update: 2022-01-20