Image Detection Model for Construction Worker Safety Conditions using Faster R-CNN

Saudi, Madihah Mohd; Ma’arof, Aiman Hakim; Ahmad, Aminah; Saudi, Ahmad Shakir Mohd; Ali, Mohd Hanafi; Narzullaev, Anvar; Ghazali, Mohd Ifwat Mohd

doi:10.14569/ijacsa.2020.0110632

Cited by 19 publications

(9 citation statements)

References 16 publications

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“…Similarly, Saudi et al [28] suggested the Faster Region-based Convolutional Neural Networks (R-CNN) algorithm provides an image scanning model for worker protection based on conformity with the PPE. Meanwhile, Wang et al [29] Cases of COVID-19 are identified using a chest X-ray.…”

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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“…Using the Faster R-CNN algorithm, provides an image detection model about the safety conditions of the employees [8]. The MIT Places Database is used as a three-class training dataset containing helmet, vest, and boots in the experiments.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the studies conducted in previous years suggested methods such as RCNN, faster RCNN [5,7,8] that can be highly accurate, but not so fast for real-time use. In addition, there are approaches using the SSD [4] and Yolo [6,10,12] families, which are widely used in real-time applications.…”

Section: Motivationmentioning

confidence: 99%

An Efficient and Fast Lightweight-Model with ShuffleNetv2 Based on YOLOv5 for Detection of Hardhat-Wearing

Cengil¹,

Çınar²,

Yıldırım³

2022

RCES

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Traumatic brain injuries and collisions from falls and electric shocks are among the leading causes of construction deaths. Helmets play an important role in protecting working people from accidents. However, wearing a hard hat in real life is often not strictly enforced among those who try. Therefore, it is important to check this and ensure that a helmet is worn. Today, the use of artificial intelligence-based object recognition systems has become widespread due to the advantages it provides. In this article, a one-step object detection approach based on deep learning is proposed to detect helmet use and control helmet wearing status. The model is based on the YOLOv5 architecture. In the feature extraction step of the method, ShuffleNetv2, which is a lightweight model for a fast detector, is used. The presented model has been examined on the Hard Hat Workers dataset. The architecture provided a recall value of 0.942 precision 0.91 in the corresponding dataset. The obtained results showed that the recommended model is suitable for use on construction sites to check whether a helmet is fitted.

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“…In order to reduce safety risks on construction sites, existing research work mainly focused on detecting the construction workers [10], [24] and whether they are wearing the protective equipment properly [8], [9], [25]- [27]. Faster R-CNN was used to accurately and rapidly detect construction workers in [10].…”

Section: Related Workmentioning

confidence: 99%

“…In order to improve the detection performance of helmets on construction sites, an extra prediction scale was added to the YOLOv5 in [27]. In addition to helmets and safety vests, safety boots were also detected in [25] by employing the Faster R-CNN algorithm. However, the datasets in the above work only contain the aforementioned items.…”

Section: Related Workmentioning

confidence: 99%

CORY-Net: Contrastive Res-YOLOv5 Network for Intelligent Safety Monitoring on Power Grid Construction Sites

Peng

Yin

et al. 2021

IEEE Access

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In power grid construction projects, ensuring the safety of construction workers by eliminating potential risks has always been important yet difficult. Thanks to the breakthrough in deep learning, it becomes possible to adopt deep learning based object detection technologies to enable intelligent safety monitoring on power grid construction sites. However, due to the complex terrain of the power grid construction site, there is a lack of a dataset of power grid construction scenes. In this paper, a high-quality power grid construction dataset is constructed. In order to reduce the expensive annotation work, we propose to adopt contrastive self-supervised learning to pretrain a feature extraction encoder and integrate it with YOLOv5 by modifying its backbone network for safety-related object detection. The proposed network is referred to as Contrastive Res-YOLOv5 Network (CORY-Net). Experimental results show that contrastive self-supervised pretraining can efficiently improve the object detection performance on the power grid construction dataset and speed up the convergence of the model. With only 1400 pretraining epochs, the proposed CORY-Net outperforms the original YOLOv5 by contributing 1.32% to the mAP@.5 and 3.54% to the mAP@.5:.95. Compared to the supervised object detection benchmarks, the proposed CORY-Net achieves the highest mAP@.5 in 9 out of 22 categories of targets and the highest overall mAP@.5.

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Image Detection Model for Construction Worker Safety Conditions using Faster R-CNN

Cited by 19 publications

References 16 publications

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

An Efficient and Fast Lightweight-Model with ShuffleNetv2 Based on YOLOv5 for Detection of Hardhat-Wearing

CORY-Net: Contrastive Res-YOLOv5 Network for Intelligent Safety Monitoring on Power Grid Construction Sites

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