Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (6): 13-29.DOI: 10.3778/j.issn.1002-8331.2207-0434

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review on Application of Deep Learning in Helmet Wearing Detection

GAO Teng, ZHANG Xianwu, LI Bai   

  1. Key Laboratory of Signal Detection and Processing, School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
  • Online:2023-03-15 Published:2023-03-15



  1. 新疆大学 信息科学与工程学院 信号检测与处理重点实验室,乌鲁木齐 830046

Abstract: Driven by deep learning, many approaches to object detection have made great progress in the field of industrial security, and the study of helmet-wearing detection has gradually become a significant topic in intelligent image recognition. In order to comprehensively analyze the research status of deep learning technology in helmet wearing detection task, and to facilitate follow-up scientific research personnel to carry out research work, this paper analyzes the state-of-the-art helmet-wearing detection algorithms under deep learning conditions proposed by domestic and foreign scholars in recent years and compares their advantages and limitations. This paper is structured in three sections:the establishment and usage of databases, the predominate algorithms for helmet-wearing detection, the current challenges in the field of helmet-wearing detection. The future research direction of helmet wearing detection field is prospected, and the future research focus in this field is put forward.

Key words: deep learning, object detection, wearing safety helmet detection, industrial security

摘要: 在深度学习的推动下,目标检测方法在工业安防领域取得了很大的进展,安全帽佩戴检测任务逐渐成为智能图像识别领域的一项重要研究课题。为了综合分析深度学习技术在安全帽佩戴检测任务中的研究现状,方便后续科研人员开展研究性工作。对近年来国内外学者在深度学习环境下的安全帽佩戴检测算法总结归纳,对比分析这些方法的优点和局限性。分别从数据集的建立和用途、安全帽佩戴检测主要检测算法归纳、当前安全帽佩戴检测领域的难点这三个方面进行分析。对安全帽佩戴检测领域未来的研究方向进行展望,并提出该领域今后研究重点。

关键词: 深度学习, 目标检测, 安全帽佩戴检测, 工业安防