文章基本信息

标题：Image Detection Model for Construction Worker Safety Conditions using Faster R-CNN
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作者：Madihah Mohd Saudi ; Aiman Hakim Ma’arof ; Azuan Ahmad 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：6
DOI：10.14569/IJACSA.2020.0110632
出版社：Science and Information Society (SAI)
摘要：Many accidents occur on construction sites leading to injury and death. According to the Occupational Safety Health Administration (OSHA), falls, electrocutions, being struck-by-objects and being caught in or between an object were the four main causes of worker deaths on construction sites. Many factors contribute to the increase in accidents, and personal protective equipment (PPE) is one of the defense mechanisms used to mitigate them. Thus, this paper presents an image detection model about workers’ safety conditions based on PPE compliance by using the Faster Region-based Convolutional Neural Networks (R-CNN) algorithm. This experiment was conducted using Tensorflow involving 1,129 images from the MIT Places Database (from Scene Recognition) as a training dataset, and 333 anonymous dataset images from real construction sites for evaluation purposes. The experimental results showed 276 of the images being detected as safe, and an average accuracy rate of 70%. The strength of this paper is based on the image detection of the three PPE combinations, involving hardhats, vests and boots in the case of construction workers. In future, the threshold and image sharpness (low resolution) will be two main characteristics of further refinement in order to improve the accuracy rate.
关键词：PPE; OSH; accident; construction site; image detection; faster R-CNN