Worker’s Helmet Recognition and Identity Recognition Based on Deep Learning

Wang, Jie; Zhu, Guangzu; Wu, Shuhao; Luo, Chi-Wen

doi:10.4236/ojmsi.2021.92009

Cited by 14 publications

(6 citation statements)

References 43 publications

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“…Nath et al [23] proposed a real-time You-Only-Look-Once (YOLO) architecture for the verification of PPE compliance of workers, i.e., whether or not a worker was wearing a hard hat, vest, or both. Moreover, human identity recognition and helmet detection was performed using YOLOv3 architecture in [24]. Wang et al [25] compared the performance of various architectures of the YOLO family (YOLOv3 [16], YOLOv4 [26], and YOLOv5 [27]) on a custom dataset, named as the CHV dataset, and found that YOLOv5x had a superior performance as compared to other models.…”

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

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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.

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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

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“…DIOU-NMS was employed to replace the traditional NMS, which improved the detection ability of the model for occluded workers and small targets. In [1,19], YOLOv3 and YOLOv4 algorithms were adopted to detect helmet wearing and worker falls, respectively. In addition to personnel detection, the YOLO detector was combined with UAV to perform real-time detection of the designated area [20,21], overcoming the problem of target omission and incorrect detection caused by pedestrian density and pedestrian occlusion under a fixed viewing angle.…”

Section: One-stage Detection Methodsmentioning

confidence: 99%

“…In terms of performance levels, this method surpasses existing approaches such as histogram of oriented gradients [41], scale-invariant feature transform [42], speeded-up robust features [43], oriented fast and rotated binary robust independent element features [44], and binary robust invariant scalable key points [45]. Wearing safety helmets was also considered as the focus of detection for construction personnel [1,2]. The study trained the safety-helmet detection model through a Faster R-CNN and expanded the original dataset through the construction of a new dataset, enriching the applicability of the detector in various environments.…”

Section: Two-stage Detection Methodsmentioning

confidence: 99%

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CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s

Hao,

Zhang,

et al. 2024

Meas. Sci. Technol.

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To address the problem of the frequent occurrence of major casualties during construction, a lightweight multi-target detection model based on YOLOv5s, named CaSnLi-YOLO, was proposed for the detection of multiple targets consisting of construction workers and various mechanical equipment at construction sites. In this study, the introduction of the Coordinate Attention (CA) mechanism along with DWConv and C3Ghost based on GhostNet, enhanced the expression and detection accuracy of the model, effectively reducing the number of parameters. Considering the environmental characteristics of construction sites, a detection box filtering strategy based on Soft Non-Maximum Suppression (Soft-NMS) was employed, to effectively alleviate the issue of missed detections of occluded targets. Experimental results demonstrate the significant superiority of the proposed CaSnLi-YOLO over current mainstream detection models, such as faster region-based convolutional neural network (Faster R-CNN) and single-shot detector (SSD). The proposed CaSnLi-YOLO has a parameter number of 5.96×106, which is 15.2% less than that of the original YOLOv5s model, further improving precision, recall rate, mAP@0.5, and mAP@0.5:0.95 by 0.6%, 0.6 %, 0.2%, and 2.3%, respectively. The improved YOLOv5s model proposed in this study achieved significant improvements in multi-target detection at construction sites. The model demonstrated effective enhancements while significantly reducing parameter count and is expected to be deployed in small-edge devices for real-time security monitoring at construction sites.

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“…Authors try to make the CV more trustable in the real world by doing an alarm system by integrating it and also a reporting system with a certain time period. Wang et al (2021a) used human identity recognition and helmet detection using YOLOv3 in a construction site. Nath, Behzadan & Paal (2020) presented a PPE detector using YOLOv3 algorithm.…”

Section: Related Workmentioning

confidence: 99%

PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites

Ferdous

Ahsan

2022

PeerJ Computer Science

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With numerous countermeasures, the number of deaths in the construction industry is still higher compared to other industries. Personal Protective Equipment (PPE) is constantly being improved to avoid these accidents, although workers intentionally or unintentionally forget to use such safety measures. It is challenging to manually run a safety check as the number of co-workers on a site can be large; however, it is a prime duty of the authority to provide maximum protection to the workers on the working site. From these motivations, we have created a computer vision (CV) based automatic PPE detection system that detects various types of PPE. This study also created a novel dataset named CHVG (four colored hardhats, vest, safety glass) containing eight different classes, including four colored hardhats, vest, safety glass, person body, and person head. The dataset contains 1,699 images and corresponding annotations of these eight classes. For the detection algorithm, this study has used the You Only Look Once (YOLO) family’s anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval. Moreover, this study found that the YOLOX-m model yields the highest mean average precision (mAP) than the other three versions of the YOLOX.

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Worker’s Helmet Recognition and Identity Recognition Based on Deep Learning

Cited by 14 publications

References 43 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

CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s

PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites

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