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建筑工地安全帽佩戴检测算法研究综述
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Abstract:
为了减少建筑工地事故的发生，提高建筑项目的安全保障，基于深度学习的建筑工人安全帽佩戴目标检测方法成为了一个重要的研究领域。该方法通过利用建筑工地的监控实时提取图像和视频信息，并自动识别工人是否正确佩戴安全帽，具有较高的准确率与实时性，可提升建筑行业安全管理的智能化水平。本综述旨在综合分析近年来在深度学习环境下的安全帽佩戴检测算法研究现状，分别从数据集与评价指标、两阶段目标检测、单阶段目标检测及改进等方面总结归纳国内外学者的研究成果，分析这些方法的优点、局限性以及当前的难点，并给出建议和展望，为后续研究者提供参考和借鉴。
In order to reduce the occurrence of construction site accidents and improve the safety and security of construction projects, the deep learning-based target detection method for construction workers’ helmet wearing has become an important research field. The method extracts image and video information in real time by utilizing the surveillance of the construction site and automatically identifies whether the worker is wearing the helmet correctly, which has high accuracy and real-time performance, and can improve the intelligent level of safety management in the construction industry. This review aims to comprehensively analyze the current research status of helmet wearing detection algorithms under deep learning environment in recent years, summarize and generalize the research results of scholars at home and abroad from the aspects of dataset and evaluation indexes, two-phase target detection, single-phase target detection, and improvement, respectively, and analyze the advantages, limitations, and current difficulties of these methods, as well as give suggestions and outlooks, so as to provide references and suggestions for the subsequent researchers.
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