A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3

Wang, Haikuan; Hu, Zhaoyan; Guo, Yuanjun; Yang, Zhile; Zhou, Feixiang; Xu, Peng

doi:10.3390/app10196732

Cited by 44 publications

(22 citation statements)

References 26 publications

(33 reference statements)

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“…The precision and recall of the system were recorded as 95% and 77%, respectively. Wang et al [ 18 ] proposed a safety helmet detection model trained on a total of 10,000 images captured by 10 different surveillance cameras at construction sites. In the experiment’s first phase, the authors employed the YOLOv3 architecture [ 9 ] and achieved an mAP 0.5 of 42.5%.…”

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

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show abstract

“…Zhou et al [7] proposed a helmet detection algorithm based on the attention mechanism (AT-YOLO) for objects with small scales and obstructions. Wang et al [8] presented a novel SHW detection model on the basis of improved YOLOv3 to heighten the capability of target detection on the construction site. Li et al [9] developed a method based on the SSD-MobileNet algorithm for real-time detection of a safety helmet at the construction site.…”

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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“…Because of this, they are more feasible for real-world application [35]. Meanwhile, the networks of YOLOv2 and YOLOv3-which are single-stage models-have been widely used for object detection [36][37][38][39][40][41]. Consequently, existing single-stage models can be adapted to detect insulators by transferring learning strategies.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Liu

et al. 2021

Electronics

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Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

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A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3

Cited by 44 publications

References 26 publications

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection

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

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

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