重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (2): 206-214.doi: 10.3969/j.issn.1674-8425(z).2023.02.023

• 信息·计算机 • 上一篇    下一篇

结合关键点和注意力机制的人员着装检测方法

孔华永,聂志勇,隋立林   

  1. 1.国家能源集团信息公司 综合自动化部,北京 100011; 2.武汉大学 测绘遥感信息工程国家重点实验室,武汉 43007
  • 出版日期:2023-03-21 发布日期:2023-03-21
  • 作者简介:孔华永,男,硕士,高级工程师,主要从事煤炭电力行业信息化自动化研究,Email:11688110@chnenergy.com. cn;通讯作者 聂志勇,男,工程师,主要从事能源行业生产控制系统、人工智能机器视觉研究,Email:11688251@ chnenergy.com.cn。

Personnel dress detection method with human keypoints and attention mechanism

  • Online:2023-03-21 Published:2023-03-21

摘要: 针对工业生产安全系统中缺乏自动监管功能,设计了一种基于人体关键点定位和 图像区域注意力机制的人员着装检测算法,对工地、码头、矿场等施工场所的工作人员进行工装 佩戴标准检测,从而保障工作人员安全。结合人体姿态估计算法对人员着装区域进行定位,提 出一种基于图像区域的注意力机制对服装特征进行有效表征,将复杂的人员着装检测任务解耦 为目标检测和图像分类任务,提高了工业场景中的人员着装检测效率和性能。在 MSCOCO及 自定义煤矿场景着装检测数据集上的实验证明,提出的模型在人员定位和着装检测任务上均取 得了优异的效果(MSCOCO数据集上 AP50达到最优),具有较强的鲁棒性,可以适用于多种复 杂环境。

关键词: 人员着装检测, 人体姿态估计, 注意力机制

Abstract: In view of a lack of automatic supervision functions in industrial production safety systems, this paper designs a personnel dress detection algorithm for operators in construction sites, docks, mines and other construction sites, which is based on the localization of human keypoints and the attention mechanism of image areas to ensure the safety by carrying out the standard test for uniform wearing. The method innovatively combines the human pose estimation to locate the dress area, proposes an attention mechanism based on the image areas to effectively represent the features,and decouples the complex dress detection task into object detection and image classification tasks. The proposed approach improves the efficiency and performance of personnel dress detection in industrial scenarios. The experiments on the MSCOCO anddress detection datasets in the customizedcoal mine scene show that the proposed model achieves excellent results in personnel localization and dress detection tasks (AP50 is the best on the MSCOCO dataset). It is robust and can be applied to a variety of complex environments.

中图分类号: 

  • X913