Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

Abstract

According to the Center for Construction Research and Training (CPWR) and the Korea Occupational Safety & Health Agency (KOSHA), falls from ladders are a leading cause of fatalities. The current safety inspection process to enforce height-related rules is manual and time-consuming. It requires the physical presence of a safety manager, for whom it is sometimes impossible to monitor an entire area in which ladders are being used. Deep learning-based computer vision technology has the potential to capture a large amount of useful information from a digital image. Therefore, this paper presents a deep learning-based height assessment method using a single known value in an image to measure working height, monitor compliance to safety rules, and ensure worker safety. The proposed method comprises (1) extraction of safety rules from the KOSHA database related to the A-type ladder; (2) object detection (Single Shot Multibox Detector SSD) (3) a height-computing module (HCM) to estimate the working height of the worker (how high a worker is from the ground); and (4) classification of worker behavior (using the developed SSD-based HCM) based on the best practices derived from the KOSHA database. The developed algorithm has been tested on four different scenarios based on KOSHA safety rules, with heights ranging from under 1.2 m to over 2 m. Additionally, the proposed method was evaluated on 300 images for binary classification (safe and unsafe) and achieved an overall accuracy of 85.33%, verifying its feasibility for intelligent height estimation and compliance monitoring. INDEX TERMS Falls from ladders, vision intelligence-based monitoring, construction safety rules, visionbased height estimation, deep learning. I. INTRODUCTIONConstruction work is likely to expose workers to hazardous situations because of its distinctive, dynamic, and complex nature. Compared with the workers in other industries, those in the construction industry are more prone to occupational accidents that could potentially lead to fatalities [1]-[3]. The most common construction accidents include fall from height (FFH), collision with objects, electrocution, and being stuckThe associate editor coordinating the review of this manuscript and approving it for publication was Turgay Celik . in between machinery. Among these, FFH is the most frequent cause of accidents at construction sites. According to the U.S. Bureau of Labor Statistics, the number of fatalities due to falls to a lower level increased by 26% from 2011 to 2016, which includes ladders (836 fatal injuries) and rooftops (763). The most common height of a fall was reported as 30 ft (658 deaths) [4]. In addition, the Center for Construction Research and Training (CPWR) reports that ladder (93)-related accidents were a leading cause of fatal injuries from 2011 to 2019 [5]. Moreover, in South Korea, industrial accidents are increasing, with accidents at construction sites

Rotation Error Detection of Gallium Nitride (GaN) Substrate in MBE Utilizing Ensemble Learning

“…AI is one of the cutting-edge technologies of Industrial Revolution (IR) 4.0, which has been adopted in all aspects of life, such as health, transportation, construction, security, sports, etc. , Data set preparation is one of the crucial steps while working with ML- and DL-related problems. However, obtaining the labeled data set from an open source is still challenging in many domains, including semiconductors.…”

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

“…Various colors are used to denote distinct clusters, while the lines connecting the circles illustrate the interconnectedness between authors and their affiliations through crossreferencing [30].The filtering condition for this analysis was a minimum of three publications, and the size of the bubbles in the figures indicates that Hong Kong Polytechnic University [31]- [51] and Huazhong University of Science and Technology [32]- [34], [36], [38], [42], [50]- [58] are currently leading the field. Other institutions conducting research in this area include Chung Ang University [59]- [65], University of Illinois [66]-[73], Dalian University of Technology [74]- [79], and more. Figure 5 highlights that Li Heng's team [31]- [36], [38], [39], [41]- [51] at Hong Kong Polytechnic University and Luo Hanbin's team [33], [42], [52]- [58] at Huazhong University of Science and Technology are currently leading the field, with similar colors representing the same research area.…”

“…During the Covid-19 pandemic, Chian et al [139] also employed CenterNet to monitor safe distances between construction workers. Ladder climbing poses a potential fall risk, which has been addressed by Han et al [70], Anjum et al [59], Ding et al [58], and Chen et al [140] using various computer vision methodologies. Furthermore, several computer vision-based studies have contributed to the recognition of unsafe behaviors, encompassing safety risk determination [71], [141], fall detection [142], and identification of unsafe actions [48], [65], [143], [144].…”

Ensuring Safety in Industrial Robots: Issues, Consequences and Solutions

“…Additionally, they provide data on the count of workdays lost by operators who sustained injuries in these occurrences. The data regarding robot accidents in this study were collected from the official data book on workplace fatalities presented by the Ministry of Employment and Labor (MOEL) in Korea [13], as well as accident investigation reports from 2009 to 2019 obtained from the Korea Occupational Safety and Health Agency (KOSHA). We have analyzed various statistical data obtained from the MOELwebpage, which regularly provides updated data on injuryrelated information, and IA on a monthly, quarterly, and annual basis.…”