Real-time Personal Protective Equipment (PPE) Detection Using YOLOv4 and TensorFlow

Protik, Adban Akib; Rafi, Amzad Hossain; Siddique, Shahnewaz

doi:10.1109/tensymp52854.2021.9550808

Cited by 20 publications

(15 citation statements)

References 6 publications

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“…It loses about 3% of its mAP value, and the frame rate has been increased. Furthermore, Protik et al [39] proposed a method by using the YOLOv4 object detection model to find some PPE for COVID-19. Similarly, Torres et al [40] proposed four possible classes to perform the classification for which two approaches are used.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Kisaezehra¹,

Farooq²,

Bhutto³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain injuries in people present at the site in general and the workers in particular. It is one of the leading causes of human fatalities at construction sites. In the United States, the Occupational Safety and Health Administration (OSHA) requires construction companies through safety laws to ensure the use of well-defined personal protective equipment (PPE). It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible. However, such monitoring through technological aids or automated tools is very much possible. The present study describes a systematic strategy based on deep learning (DL) models built on the You-Only-Look-Once (YOLOV5) architecture that could be used for monitoring workers' hard hats in real-time. It can indicate whether a worker is wearing a hat or not. The proposed system uses five different models of the YOLOV5, namely YOLOV5n, YOLOv5s, YOLOv5 m, YOLOv5l, and YOLOv5x for object detection with the support of PyTorch, involving 7063 images. The results of the study show that among the DL models, the YOLOV5x has a high performance of 95.8% in terms of the mAP, while the YOLOV5n has the fastest detection speed of 70.4 frames per second (FPS). The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Kisaezehra¹,

Farooq²,

Bhutto³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Object detection is a technique commonly used in computer vision for a variety of problems. Significant prior work has been done to use object detection to detect the use of PPE [11][12][13][14]. [11,12] use convolutional neural networks (CNNs) to detect the use of PPE (hard hat, vest, and shoes), while [13] can detect PPE (hard hat and vest) but also analyzes the safety level using the present PPE.…”

Section: Related Workmentioning

confidence: 99%

“…If either a hard hat or vest is missing, then the output states the missing PPE. [14] considers PPE detection in the context of COVID-19 and detects PPE such as a face mask, face shield, and gloves. However, these prior methods have not been combined with activity detection methods to create a platform-based approach.…”

Section: Related Workmentioning

confidence: 99%

An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents

Adhikesaven¹

2022

Preprint

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution. Problem and MotivationEvery year, almost two million people die from work-related accidents or diseases [1], and another 317 million suffer from work-related injuries [2]. The economic cost of these illnesses, injuries, and deaths is nearly three trillion dollars annually [3]. Beyond the direct toll on affected workers and medical expenses, workplace injuries lead to loss of productivity for all involved in the accident, property damage, and ultimately worsening morale among all workers [4]. Accounting for 39 percent of work related injuries, overexertion is the most common cause of these injuries [5]. Overexertion consists of lifting, lower, and other repetitive actions, which are all ergonomic issues. Another 31 percent of workplace injuries comes from contact with other objects [5], making the use of personal protective equipment (PPE) critical.Climate change has a strong impact on worker safety and health. Industrial workers are often most adversely affected by climate change and are exposed to physically demanding work for a longer period of time [6,7]. Both outdoor and indoor factory workers face the impacts of increased heat [6,7], which is often worsened by the lack of air conditioning and ventilation in many factories [8]. As global warming increases due to climate change, the impacts of extreme heat lead to many workplace injuries by lowering worker concentration and increasing fatigue [9]. For example, extreme heat has caused more than 20 thousands additional workplace injuries annually in California alone [9]. Overall, environmental heat causes over 170 thousand work-related injuries in the United States annually, making it the third highest cause of worker injuries [10]. It also contributes to as much as 2,000 deaths annually, possibly making heat exposure the leading cause of worker deaths [10]. Given the severe impact of climate change on workers, it is important to improve workplace safety and protect workers ...

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“…Due to the Covid-19 pandemic and the need to check if people are wearing the required personal protective equipment (PPE), several masks and gloves recognition systems based on Deep Learning have been reported in the literature [20] [21] [22] [23] [24]. However, no existing work addressed the problem of recognizing masks and gloves thrown in the street.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [22] proposed a Deep Learning approach in order to recognize whether or not people are wearing PPE. More specifically, they adopted the YOLOv4 [33] model.…”

Section: A Detection Of Masks and Gloves Worn By Peoplementioning

confidence: 99%

A Computer Vision System for Street Sweeper Robot

Bchir¹,

Almasoud²,

Alyahya³

et al. 2022

IJACSA

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With the spread of Covid-19, more people wear personal protective equipment such as gloves and masks. However, they are littering them all over streets, parking lots and parks. This impacts the environment and damages especially the marine ecosystem. Thus, this waste should not be discarded in the environment. Moreover, it should not be recycled with other plastic materials. Actually, they have to be separated from regular trash collection. Furthermore, littering gloves and masks yields more workload for street cleaners and presents potential harm for them. In this paper, we design a computer vision system for a street sweeper robot that picks up the masks and gloves and disposes them safely in garbage containers. This system relies on Deep Learning techniques for object recognition. In particular, three Deep Learning models will be investigated. They are: You Only Look Once (YOLO) model, Faster Region based Convolutional Neural Network (Faster R-CNN) and DeepLab v3+. The experiment results showed that YOLO is the most suitable approach to design the proposed system. Thus, the performance of the proposed system is 0.94 as F1 measure, 0.79 as IoU, 0.94 as mAP, and 0.41 s as Time to process one image.

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Real-time Personal Protective Equipment (PPE) Detection Using YOLOv4 and TensorFlow

Cited by 20 publications

References 6 publications

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

An Industrial Workplace Alerting and Monitoring Platform to Prevent Workplace Injury and Accidents

A Computer Vision System for Street Sweeper Robot

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