Review of Deep Learning Algorithms for One-Stage Safety Helmet Detection

doi:10.3778/j.issn.1002-8331.2312-0034

Abstract

Abstract: Real-time detection of safety helmet-wearing is an essential part of smart construction sites and smart traffic, safety helmet detection based on deep learning has gradually replaced the traditional detection methods, and has made significant progress in accuracy, performance, and efficiency. It has been widely used in real scenarios. To facilitate the future research of safety helmet-wearing algorithms, the research status of object detection algorithms for safety helmets in various application scenarios is analyzed comprehensively. Firstly, the history of the object detection algorithm is summarized. Secondly, the advantages and disadvantages of different algorithms and optimizations are analyzed, and the lightweight safety helmet detection algorithms are discussed. Finally, according to the shortages of the current object detection algorithm applied in the actual scene, the future direction of a deep learning algorithm for safety helmet detection is prospected.

Key words: deep learning, object detection algorithm, safety helmet detection

摘要： 安全帽佩戴实时检测是智慧工地和智慧交通必不可少的一部分，基于深度学习的安全帽检测逐渐取代了传统的检测方法，在精度、性能和效率等方面取得了显著进展，在现实场景中有了广泛的应用。为了便于安全帽算法的研究，综合分析了各应用场景中安全帽目标检测算法的研究现状。总结了目标检测算法的发展历史；对近年来国内外学者的安全帽检测算法研究进行归纳，对比总结了不同算法不同优化的优缺点，着重分析了安全帽检测算法的轻量化方法；根据目前目标检测算法在实际应用场景中出现的不足，对安全帽检测的深度学习算法的未来研究方向进行了展望。

关键词: 深度学习, 目标检测算法, 安全帽检测

GUAN Hanyu, LING Yun, WANG Shulei. Review of Deep Learning Algorithms for One-Stage Safety Helmet Detection[J]. Computer Engineering and Applications, 2024, 60(16): 61-75.

管含宇, 凌云, 汪舒磊. 单阶段安全帽检测深度学习算法综述[J]. 计算机工程与应用, 2024, 60(16): 61-75.

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