一种高精度的卷积神经网络安全帽检测方法

李天宇; 李栋; 陈明举; 吴浩; 刘益岑

doi:10.37188/CJLCD.2020-0309

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一种高精度的卷积神经网络安全帽检测方法

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- 一种高精度的卷积神经网络安全帽检测方法
- High precision detection method of safety helmet based on convolution neural network
- 液晶与显示 2021年36卷第7期页码：1018-1026
- 作者机构：
  
  1.四川轻化工大学人工智能四川省重点实验室, 四川宜宾 644000
  2.国网四川省电力公司电力研究院, 四川成都 610000
- 作者简介：
  
  [ "李天宇(1996-), 男, 硕士研究生, 2018年于四川轻化工大学获得学士学位, 主要从事计算机视觉、目标检测方向的研究。E-mail:litianyu207@163.com" ]
  [ "陈明举(1982-), 男, 重庆人, 博士研究生, 副教授, 2007年于重庆邮电大学获得硕士学位, 主要从事智能信息处理、图像处理和机器学习研究。E-mail: chenmingju@suse.edu.cn" ]
- 基金信息：
  
  国家电网公司科技攻关项目(521997180016);四川省科技厅项目(2017JY0338);四川省科技厅项目(2019YJ0477);四川省科技厅项目(2018GZDZX0043);四川省科技厅项目(2018JY0386);四川省科技厅项目(2020YFG0178);人工智能四川省重点实验室项目(2019RYY01);四川轻化工大学研究生创新基金项目(y2020020)
- DOI：10.37188/CJLCD.2020-0309
  中图分类号： TP391.4;TH691.9
- 收稿日期：2020-11-18，
  
  修回日期：2020-12-07，
  
  纸质出版日期：2021-07
- 稿件说明：
移动端阅览
李天宇, 李栋, 陈明举, 等. 一种高精度的卷积神经网络安全帽检测方法[J]. 液晶与显示, 2021,36(7):1018-1026.

Tian-yu LI, Dong LI, Ming-ju CHEN, et al. High precision detection method of safety helmet based on convolution neural network[J]. Chinese journal of liquid crystals and displays, 2021, 36(7): 1018-1026.
李天宇, 李栋, 陈明举, 等. 一种高精度的卷积神经网络安全帽检测方法[J]. 液晶与显示, 2021,36(7):1018-1026. DOI： 10.37188/CJLCD.2020-0309.

Tian-yu LI, Dong LI, Ming-ju CHEN, et al. High precision detection method of safety helmet based on convolution neural network[J]. Chinese journal of liquid crystals and displays, 2021, 36(7): 1018-1026. DOI： 10.37188/CJLCD.2020-0309.

摘要

在复杂的施工环境中，基于机器视觉技术的安全帽佩戴检测算法常常出现漏检、误检，其检测能力有限。为提高安全帽佩戴检测的精度，本文建立了一种基于注意力机制的双向特征金字塔的安全帽检测卷积神经网络。为兼顾卷积神经网络中的浅层位置信息和深层语义信息的表达能力，实现对弱小安全帽目标的检测能力，该网络将跳跃连接和注意力机制CBAM技术引入双向特征融合的特征金字塔网络PANet模块中，构建基于注意力机制的双向特征金字塔模块CPANet。为提高模型的收敛能力，采用了CIoU来代替IoU进行优化锚框回归预测，降低该网络的训练难度。对比实验表明，本文建立的检测网络比YOLOv3、RFBNet、SSD、Faster RCNN的mAP值分别提高了0.82，4.43，23.12，23.96，检测速度达到21 frame/s，实现了施工现场安全帽佩戴实时高精度检测。

Abstract

In a complex construction environment

the helmet wearing recognition algorithm based on machine vision technology often fails and misdetects

and its recognition ability is limited. In order to improve the accuracy of helmet wearing recognition

this paper establishes a helmet recognition convolutional neural network based on the bidirectional feature pyramid of the attention mechanism isproposed. In order to improve the expression ability of shallow position information and deep semantic information in the convolutional neural network

and increase the recognition rate of vague and small helmets

the network introduces the jump connection and the attention mechanism CBAM technology into the bidirectional feature fusion feature pyramid network PANet module

and bidirectional feature pyramid module based on the attention mechanism is constructed. In order to improve the convergence ability of the model

CIoU is used instead of IoU to optimize the anchor frame regression prediction

which reduces the complexity of the network training. The results of comparative experiment show that the mAP value of our proposed recognition network is 0.82

4.43

23.12 and 23.96 higher than that of YOLOv3

RFBNet

SSD

Faster RCNN

respectively

and its detection speed reaches 21 frame/s

thus satisfy with height real-time accuracy of helmet recognition in the construction environment.

关键词

Keywords

references

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