Safety Helmet Wearing Detection Based On Deep Learning

Long, Xitian; Cui, Wenpeng; Zheng, Zhe

doi:10.1109/itnec.2019.8729039

Cited by 61 publications

(15 citation statements)

References 6 publications

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“…The label confidence of 0.982 was achieved by testing it on 689 images. Moreover, Long et al [ 26 ] proposed a deep-learning-based detection of safety helmet wearing using 5229 images, acquired from the Internet and various power plants (including power plants under construction). The proposed system was based on SSD, and an mAP 0.5 of 78.3% was achieved on the test images and compared with SSD, which was 70.8% using an IoU of 0.5.…”

Section: Related Workmentioning

confidence: 99%

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

Otgonbold

Gochoo

Alnajjar

et al. 2022

Sensors

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Wearing a safety helmet is important in construction and manufacturing industrial activities to avoid unpleasant situations. This safety compliance can be ensured by developing an automatic helmet detection system using various computer vision and deep learning approaches. Developing a deep-learning-based helmet detection model usually requires an enormous amount of training data. However, there are very few public safety helmet datasets available in the literature, in which most of them are not entirely labeled, and the labeled one contains fewer classes. This paper presents the Safety HELmet dataset with 5K images (SHEL5K) dataset, an enhanced version of the SHD dataset. The proposed dataset consists of six completely labeled classes (helmet, head, head with helmet, person with helmet, person without helmet, and face). The proposed dataset was tested on multiple state-of-the-art object detection models, i.e., YOLOv3 (YOLOv3, YOLOv3-tiny, and YOLOv3-SPP), YOLOv4 (YOLOv4 and YOLOv4pacsp-x-mish), YOLOv5-P5 (YOLOv5s, YOLOv5m, and YOLOv5x), the Faster Region-based Convolutional Neural Network (Faster-RCNN) with the Inception V2 architecture, and YOLOR. The experimental results from the various models on the proposed dataset were compared and showed improvement in the mean Average Precision (mAP). The SHEL5K dataset had an advantage over other safety helmet datasets as it contains fewer images with better labels and more classes, making helmet detection more accurate.

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Section: Related Workmentioning

confidence: 99%

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

Otgonbold

Gochoo

Alnajjar

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…The use of a neural network solution to detect if workers are wearing helmets during their activities is proposed in [12,13]. In [14], Ravikiran and Sen argue that the video detection of safety gear on workers could be an adequate solution in the context of industrial safety by introducing a safety gear detection dataset consisting of 5 k images with hardhats, vests, gloves, and goggles.…”

Section: Related Workmentioning

confidence: 99%

“…This grouping includes solutions using advanced machine learning (ML) technologies to achieve their goals [12][13][14].…”

Section: Machine Learning Solutionsmentioning

confidence: 99%

An IoT-Based Mobile System for Safety Monitoring of Lone Workers

Battistoni

Sebillo

Vitiello

2021

IoT

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The European Agency for Safety and Health at Work considers Smart Personal Protective Equipment as “Intelligent Protection For The Future”. It mainly consists of electronic components that collect data about their use, the workers who wear them, and the working environment. This paper proposes a distributed solution of Smart Personal Protective Equipment for the safety monitoring of Lone Workers by adopting low-cost electronic devices. In addition to the same hazards as anyone else, Lone Workers need additional and specific systems due to the higher risk they run on a work site. To this end, the Edge-Computing paradigm can be adopted to deploy an architecture embedding wearable devices, which alerts safety managers when workers do not wear the prescribed Personal Protective Equipment and supports a fast rescue when a worker seeks help or an accidental fall is automatically detected. The proposed system is a work-in-progress which provides an architecture design to accommodate different requirements, namely the deployment difficulties at temporary and large working sites, the maintenance and connectivity recurring cost issues, the respect for the workers’ privacy, and the simplicity of use for workers and their supervisors.

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“…Fang et al [14] used the high precision, high speed, and widely applicable Faster R-CNN method to detect construction workers' NHU in different construction site conditions. Long et al [15] presented a DL approach for accurate safety helmets wearing detection by employing a single shot multibox detector. It can be seen from the above research that the integration of CV and DL technology has achieved remarkable results in the industrial field in recent years and has been maturely used in the application of helmet detection and recognition.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Vision-Based Method of Worker Identification for Industrial Internet of Things (IoT)

Wang

Chen

et al. 2022

Wireless Communications and Mobile Computing

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With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker’s head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel detection and helmet color characteristics. First, the motion objects that contain personnel and nonpersonnel are detected by the Gaussian mixture model (GMM) and extracted to generate the region of interest (RoI) images. Then, the multiple-scale histogram of oriented gradient (MHOG) features of the RoI images are extracted, and the personnel images are identified by the support vector machine (SVM). Third, the workers’ head images are obtained by the OpenPose model and personnel mask, and the GoogLeNet-based transfer learning network is established to extract the head images features and realize worker identification. This method is tested on our dataset, and the average accuracy of worker identification for multiple helmet color combinations reaches 99.43%, which is robust to workers’ angle, scale, and occlusion.

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Safety Helmet Wearing Detection Based On Deep Learning

Cited by 61 publications

References 6 publications

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

An IoT-Based Mobile System for Safety Monitoring of Lone Workers

An Intelligent Vision-Based Method of Worker Identification for Industrial Internet of Things (IoT)

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