[1]李骏峰,杨小军,张凯望.基于 YOLOX-L 算法的安全帽佩戴检测方法[J].计算机技术与发展,2022,32(09):100-106.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 016]
　LI Jun-feng,YANG Xiao-jun,ZHANG Kai-wang.Safety Helmet Wearing Detection Method Based on YOLOX-L Algorithm[J].,2022,32(09):100-106.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 016]
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基于 YOLOX-L 算法的安全帽佩戴检测方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年09期
页码:
100-106
栏目:
人工智能
出版日期:
2022-09-10

文章信息/Info

Title:
Safety Helmet Wearing Detection Method Based on YOLOX-L Algorithm
文章编号:
1673-629X(2022)09-0100-07
作者:
李骏峰; 杨小军; 张凯望
长安大学 信息工程学院,陕西 西安 710068
Author(s):
LI Jun-feng; YANG Xiao-jun; ZHANG Kai-wang
School of Information Engineering,Chang’an University,Xi’an 710068,China
关键词:
深度学习; 卷积神经网络; 目标检测; 安全帽佩戴检测; YOLOX-L 算法
Keywords:
deep learning; convolutional neural network; target detection; safety helmet wearing detection; YOLOX-L algorithm
分类号:
TP399
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 09. 016
摘要:
全帽作为生产和作业场地工人的最基本的个体防护装备,能够极大地保证工作人员的安全防护,在实际生产环境下由于多种因素造成的伤亡中,没有佩戴安全帽造成伤亡事故的占比一直很高。 为了减少相应的事故发生率,提出一种基于 YOLOX-L 算法的安全帽佩戴检测方法。 YOLOX 系列模型是当前最为先进的实时的无锚的单阶段检测器之一,在多种类别目标的检测下都具有优异的准确率和速度效果。 通过使用 YOLOX-L 模型骨干层提取多尺度的特征图,用于回归目标位置和预测类别;使用安全帽佩戴检测数据集( SHWD) ,使用冻结非冻结的训练方式训练 YOLOX-L 网络,最后选取最好的训练模型检测安全帽的类别和位置。 实验结果表明,在 SHWD 数据集检测任务中,相比较于 YOLOv3 算法,基于YOLOX-L 的 mAP 提高了 5. 41% ,查全率分别提高了 18. 51% 和 26. 44% 。 所提方法在满足高准确率和实时性要求的基础上,更少发生漏检,具有更高的查全率。
Abstract:
Safety helmet,as the most basic personal protective equipment for workers in production and operation sites,can greatly ensure the safety protection of workers. In the actual production environment,among the casualties caused by various factors,the proportion ofaccidents caused by not wearing safety helmet has been quite high. In order to reduce the accident rate, a helmet wearing detection method based on YOLOX-L algorithm is proposed. YOLOX series model is one of the most advanced real-time single-stage detectors without anchors at present,which has excellent accuracy and speed in detecting various kinds of targets. The backbone layer of YOLOX-L model is used to extract different multi-scale feature maps for target location and category prediction. In this paper,we use the data set of helmet wearing test ( SHWD) ,train the YOLOX-L network by using the frozen and unfrozen training method,and finally select the best training model to detect the category and position of helmet. Experimental results show that compared with YOLOv3 algorithm,the mAP based on YOLOX-L is improved by 5. 41% ,and the recall rate is increased by 18. 51% and 26. 44% respectively in the SHWD dataset detection task. On the basis of meeting the requirements of high accuracy and real-time,the proposed method has less missed detection and higher recall rate.

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更新日期/Last Update: 2022-09-10