Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time detection. Although a growing body of literature has developed many deep learning-based models to detect helmet for the traffic surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. In this regard, we develop a deep learning-based method for the real-time detection of a safety helmet at the construction site. The presented method uses the SSD-MobileNet algorithm that is based on convolutional neural networks. A dataset containing 3261 images of safety helmets collected from two sources, i.e., manual capture from the video monitoring system at the workplace and open images obtained using web crawler technology, is established and released to the public. The image set is divided into a training set, validation set, and test set, with a sampling ratio of nearly 8 : 1 : 1. The experiment results demonstrate that the presented deep learning-based model using the SSD-MobileNet algorithm is capable of detecting the unsafe operation of failure of wearing a helmet at the construction site, with satisfactory accuracy and efficiency.

中文翻译：

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基于卷积神经网络的工程管理中基于深度学习的安全帽检测

对工作场所进行目视检查并及时提醒戴安全帽的情况对于避免施工现场工人受伤尤为重要。视频监视系统为此目的在现场提供了大量的非结构化图像数据，但是，需要基于计算机视觉的自动解决方案来进行实时检测。尽管越来越多的文献开发了许多基于深度学习的模型来检测交通监控方面的头盔，但鉴于建筑工地的复杂场景，针对行业应用的合适解决方案却很少讨论。在这方面，我们开发了一种基于深度学习的方法，用于在施工现场实时检测安全帽。提出的方法使用基于卷积神经网络的SSD-MobileNet算法。建立并发布了一个数据集，该数据集包含从两个来源收集到的安全帽图像，这些图像来自两个来源，即从工作场所的视频监控系统进行手动捕获，以及使用网络爬虫技术获取的开放图像。该图像集分为训练集，验证集和测试集，采样率接近8：1：1。实验结果表明，使用SSD-MobileNet算法提出的基于深度学习的模型能够在施工现场检测戴头盔失败的不安全操作，准确性和效率令人满意。从工作场所的视频监控系统手动捕获并使用网络爬虫技术获取打开的图像，并将其发布给公众。该图像集分为训练集，验证集和测试集，采样率接近8：1：1。实验结果表明，使用SSD-MobileNet算法提出的基于深度学习的模型能够在施工现场检测戴头盔失败的不安全操作，准确性和效率令人满意。从工作场所的视频监控系统手动捕获并使用网络爬虫技术获取打开的图像，并将其发布给公众。该图像集分为训练集，验证集和测试集，采样率接近8：1：1。实验结果表明，使用SSD-MobileNet算法提出的基于深度学习的模型能够在施工现场检测戴头盔失败的不安全操作，准确性和效率令人满意。