SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

Otgonbold, Munkh-Erdene; Gochoo, Munkhjargal; Alnajjar, Fady; Ali, Luqman; Tan, Tan-Hsu; Hsieh, Jun-Wei; Chen, Ping-Yang

doi:10.3390/s22062315

Cited by 30 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To bridge these problems, automatic vision-based deep learning monitoring and detection techniques provide the solution. These approaches have shown promising performance in tackling the challenge of accurate safety monitoring and hazard detection problems in various applications [7][8][9][10][11][12][13][14]. Most studies focus on detecting and monitoring PPE compliance for workers' safety in the construction industry [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions

Ali

Alnajjar

Parambil

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions

Ali

Alnajjar

Parambil

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors of [17] have done the study about the helmet detection by utilizing the SHEL5K dataset. They have used the state of the art object detection models of YOLO.…”

Section: Literature Reviewmentioning

confidence: 99%

Industrial Safety Helmet Detection Using Single Shot Detectors Models and Transfer Learning

Umair¹,

Foo²

2022

Proceedings of the Multimedia University Engineering Conference (MECON 2022)

View full text Add to dashboard Cite

Personal safety is concerned to be as a crucial part for the industrial workers while working in an industrial environment. Industries provide personal protective equipment to their workers to ensure their safety, similarly the workers are also meant to wear and follow all the regulations regarding the personal protective equipment's (PPEs) provided to them. Our study provides the methodology to detect the industrial safety helmet using the surveillance cameras. In this study, we have trained two different single shot detector models i.e., Single Shot Detector (SSD) MobilenetV2 and Single Shot Detector (SSD) Resnet50 and used transfer learning methodology to detect the industrial safety helmet. We have utilized a publically available dataset from Kaggle website and utilized that dataset for the purpose of training the models. Furthermore, the models evaluation is done based on these parameters i.e., classification loss, localization loss, regularization loss and total loss. However, we concluded that the SSD Mobilenet V2 performs better than SSD Resnet50 model based on loss parameters. For SSD Mobilenet v2 we achieved a classification loss of 0.11, localization loss of 0.05, regularization loss of 0.15, and a total loss as 0.32 respectively. Moreover, the graphs for the loss of each model has also been studied.

show abstract

“…Deep learning-related technologies are increasingly integrated into people’s daily life, and object detection algorithms ( Qi et al, 2021 ; Liu et al, 2022a , b ; Xu et al, 2022 ), as a crucial component of the autonomous driving perception layer, can create a solid foundation for behavioral judgments during autonomous driving. Although object detection algorithms based on 2D images ( Bochkovskiy et al, 2020 ; Bai et al, 2022 ; Cheon et al, 2022 ; Gromada et al, 2022 ; Long et al, 2022 ; Otgonbold et al, 2022 ; Wahab et al, 2022 ; Wang et al, 2022 ) have had a lot of success at this stage, single-view images cannot completely reflect the position pose, and motion orientation of objects in 3D space due to the lack of depth information in 2D images. Consequently, in the field of autonomous driving, the focus of object detection research has increasingly switched from 2D image detection to 3D image detection and point cloud detection.…”

Section: Introductionmentioning

confidence: 99%

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

Yongxin

Zhetao²,

Tan³

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

Lidar-based 3D object detection and classification is a critical task for autonomous driving. However, inferencing from exceedingly sparse 3D data in real-time is a formidable challenge. Complex-YOLO solves the problem of point cloud disorder and sparsity by projecting it onto the bird’s-eye view and realizes real-time 3D object detection based on LiDAR. However, Complex-YOLO has no object height detection, a shallow network depth, and poor small-size object detection accuracy. To address these issues, this paper has made the following improvements: (1) adds a multi-scale feature fusion network to improve the algorithm’s capability to detect small-size objects; (2) uses a more advanced RepVGG as the backbone network to improve network depth and overall detection performance; and (3) adds an effective height detector to the network to improve the height detection. Through experiments, we found that our algorithm’s accuracy achieved good performance on the KITTI dataset, while the detection speed and memory usage were very superior, 48FPS on RTX3070Ti and 20FPS on GTX1060, with a memory usage of 841Mib.

show abstract

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

Cited by 30 publications

References 24 publications

Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions

Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions

Industrial Safety Helmet Detection Using Single Shot Detectors Models and Transfer Learning

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

Contact Info

Product

Resources

About