An Analysis on Safety Risk Judgment Patterns Towards Computer Vision Based Construction Safety Management

Abstract

Due to dynamic and constantly changing nature of construction projects, the highest accident and fatality rate makes the industry infamous in mitigating hazardous safety risks and protecting workers at jobsites. Despite of enormous efforts and serious attention by government agencies and professional bodies, current safety management still relies on traditional manual approach by auditing and supervising safety rule compliance which are infrequent, inefficient and prone to error. With the advent of new emerging technologies such as BIM, VR/AR, AI, computer vision, and big data analytics, various tech-based solutions to help manage and reduce site risks has been introduced during the last decade. Computer vision technology, in particular, has been most attractive to site safety monitoring by academics and construction startups around the globe. However, literature review has revealed that the vision-based researches are limited to object detection such as workers' PPEs and machines to help subsidize the manual approach prototypically. The purpose of this study is to propose a wide-range applicability of computer vision technologies by investigating safety risk patterns. In doing so, entire safety rules and clauses described in the Korea Occupational Safety and Health Agency (KOSHA) regulations of construction sector is reviewed and analyzed with safety experts. Four main safety risk judgment patterns were found and grouped for various vision technology applications. The remaining clauses was classified into two different types. It is expected that the findings of this study would provide an insight to researchers and developers in construction safety domain.

Construction Site Hazards Identification Using Deep Learning and Computer Vision

“…The safety and wellbeing of employees are essential targets for a successful project [4]. Employees' overall health is crucial for authorities when approving and monitoring a project.…”

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

“…According to the estimate calculated by the Centre for Construction Research and Training, a 75% probability of an accident leading to disability of a construction worker is expected within their 45-year career [4]. Aside from the humanitarian implications, these incidents result in significant financial losses that could be avoided with special safety precautions [5]. The annual cost of these enormous accidents exceeds USD 48 billion in the United States [6].…”

“…Adherence to safety rules and the implementation of industry best practices significantly reduce the likelihood of workplace accidents [9]. However, acquiring a broad range of safety information, identifying relevant rules in a safety database, and manually enforcing them is time-consuming [5].…”

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

“…Many computer vision-based algorithms have been proposed to detect objects, such as Gaussian mixture model (GMM) [10], histogram of oriented gradient (HOG) [11], and support vector machine [12]. These algorithms were also used in safety helmet detection to deal with the high fatality rates confronting the construction industry [13]. Liu et al [14] used skin color and hu moments to recognize helmet.…”

“…The development of CNN-based detectors has motivated deep learning-based safety helmet wearing detection methods [28], [29], and many researchers considered deep learning-based methods as a vital measure to deal with the construction safety management issue [13]. Two-stage detectors were first used in detecting helmet wearing.…”