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首页 > 过刊浏览>2023年第46卷第9期 >142-152. DOI:10.11835/j.issn.1000.582X.2023.09.014
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基于STA-YOLOv5的水利建造人员安全帽佩戴检测算法
作者:
作者单位:

1.重庆市西部水资源开发有限公司 重庆 401329;2.重庆大学 自动化学院，重庆 400030;3.中国建筑科学研究院有限公司, 北京 100013

作者简介:

李顺祥（1969—），男，高级工程师，主要从事水利工程建设管理、智慧水利等方向研究，(E-Mail)281849629@qq.com。

通讯作者:

王楷（1981—），男，博士，副教授，(E-mail)kaiwang@cqu.edu.cn。

基金项目:

重庆市水利科技资助项目（渝西水司文[2021]19号），重庆市技术创新与应用发展专项重点项目（cstc2021jscx-gksbX0032）。


Hydraulic construction personnel safety helmet wearing detection algorithm based on STA-YOLOv5
Author:
Affiliation:

1.Chongqing Western Water Resources Development Co., Ltd., Chongqing 401329, P. R. China;2.College of Automation, Chongqing University, Chongqing 400030, P. R. China;3.China Academy of Building Research, Beijing 100013, P. R. China

Fund Project:

Supported by Chongqing Water Science and Technology Project (No.19, Shuisiwen [2021], West Chongqing) and the Technology Innovation & Application Development Projects of Chongqing（cstc2021jscx-gksbX0032）.

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    摘要:

    在大型水利建造工程现场，存在高空坠物、塔吊转动、墙体坍塌等问题，对于建造人员人身安全造成巨大威胁，佩戴安全帽是保护建造人员的有效措施，作为工程作业中的安全管理，对建造人员进行安全帽佩戴的精确检测很有必要。针对现有安全帽检测算法在大型水利建造场景下对小且密集的安全帽目标存在漏检、检测精度较低等问题，提出一种基于STA-YOLOv5的安全帽佩戴检测算法，该算法将Swin Transformer和注意力机制引入到YOLOv5算法中，提高模型对安全帽的识别能力。实验结果表明，STA-YOLOv5算法具有更精确检测结果，识别准确率达到91.6%，较原有的YOLOv5算法有明显提升。

    Abstract:

    At the site of large-scale water conservancy construction projects, there are problems such as falling objects, tower crane rotation, wall collapse, which pose a great threat to the personal safety of construction personnel. Wearing safety helmets is an effective measure to protect construction personnel. Therefore, it is necessary for construction personnel to carry out accurate detection of helmet wearing as a safety management in engineering operations. Aiming at the problems of missing detection and low detection accuracy of small and dense helmet targets in large-scale hydraulic construction scenarios, a helmet wearing detection algorithm based on STA-YOLOv5 is proposed, which introduces Swin Transformer and attention mechanism into YOLOv5 algorithm to improve the identification ability of the model to the helmet. The experimental results show that the STA-YOLOv5 algorithm has more accurate detection results, and the recognition accuracy reaches 91.6%, which is significantly improved compared with the original YOLOv5 algorithm.

    参考文献
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李顺祥,蒋海洋,熊伶,黄才生,蒋有高,邓曦,王楷,张鹏.基于STA-YOLOv5的水利建造人员安全帽佩戴检测算法[J].重庆大学学报,2023,46(9):142-152.

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  • 收稿日期:2023-05-12
  • 在线发布日期: 2023-09-25
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