基于深度学习的安全帽佩戴检测

Xitian Long, WenpengCui Cui, Zhe Zheng
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引用次数: 35

摘要

在许多场景中,例如电站,检测流动工人是否戴安全帽对于安全问题是非常重要的。到目前为止,对安全帽佩戴检测的研究主要集中在手工制作的特征,如颜色或形状。随着深度学习的日益成功,通过训练深度卷积神经网络(deep convolutional neural network, DCNN)来准确检测目标成为一种非常有效的方法。本文提出了一种基于深度学习的安全头盔佩戴检测方法。此外,由于安全帽通常相对较小,SSD难以检测非常小的物体,因此提出了一种新颖实用的安全帽佩戴检测系统。最后,在变电所的大量令人信服的实验结果表明了我们工作的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Safety Helmet Wearing Detection Based On Deep Learning
In many scenarios, such as power station, the detection of whether wearing safety helmets or not for perambulatory workers is very essential for the safety issue. So far, research in safety helmets wearing detection mainly focused on hand-crafted features, such as color or shape. With rising success of deep learning, accurately detecting objects by training the deep convolutional neural network (DCNN) becomes a very effective way. This paper presents a deep learning approach for accurate safety helmets wearing detection in employing a single shot multi-box detector (SSD). Moreover, because of safety helmet usually relatively small and unfortunately SSD struggles in detecting very small objects, a novel and practical safety helmet wearing detecting system is proposed, Finally, extensive compelling experimental results in power substation illustrate the efficiency and effectiveness of our work.
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