An Advanced Deep Learning Approach for Safety Helmet Wearing Detection

Gu, Yuwan; Xu, Sugang; Wang, Yaru; Shi, Lin

doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00128

Cited by 16 publications

(11 citation statements)

References 8 publications

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“…Their model achieved promising results on a custom helmet dataset, highlighting the potential of YOLO architectures for this task. Gu et al (2019) [2] also explored a deep learning approach for helmet detection, using the Faster RCNN model architecture. [17] [19] The research by Jo¨nsson Hyberg & Sjo¨berg (2023) [3] investigated YOLOv8's performance in pedestrian detection, demonstrating its accuracy and efficiency.…”

Section: IIImentioning

confidence: 99%

Automating Helmet Usage Detection: A YOLOv8 Based Framework

Shevtekar,

Langhi

2024

IJRASET

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The biggest threat to Powered two-wheeler(PTW) riders is a head injury. With the humongous amount of daily commuters on such PTWs, the risk only increases substantially. In such cases, helmets reduce the risk of head injuries in accidents but not every citizen is keen on their use. In India, where the use of motorcycles is widespread, ensuring helmetuse remains a challenge. This study explores the effectiveness of YOLOv8(You Only Look Once) a state-of-the-art single-stage object detection algorithm for helmet detection on Indian roads [14]. The CNN(Convolutional Neural Networks) based technologyimplements many other efficient, reliable and quick algorithmsto train, validate and predict the object’s detection, segmentationand classification task. We leverage the use of a publicly available Indian helmet dataset from Kaggle, containing 942 images with annotations. This an image dataset of real-life powered two-wheeler riders. The dataset has images and video frames for training on 5 different objects/classes along with annotations that specify thoseobjects. This multi-class approach offers valuable insight intohelmet usage patterns paves shows us the way for real-world applications for automated traffic monitoring. Real-time helmet and number plate detection can become a game changer in trafficmonitoring and can strengthen basic safety laws

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Section: IIImentioning

confidence: 99%

Automating Helmet Usage Detection: A YOLOv8 Based Framework

Shevtekar,

Langhi

2024

IJRASET

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“…The faster region-based convolutional neural network (Faster RCNN) is utilized to detect both motorcyclists and helmets [26]. The faster RCNN equipped with the multi-scale training and increasing anchors strategies has proved to be capable of detecting helmets on different scales [27]. Taking the processing speed into consideration, YOLO is even more popular.…”

Section: Helmet Detectionmentioning

confidence: 99%

A Super-Resolution Reconstruction Driven Helmet Detection Workflow

Liu

Zhan

et al. 2022

Applied Sciences

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The degrading of input images due to the engineering environment decreases the performance of helmet detection models so as to prevent their application in practice. To overcome this problem, we propose an end-to-end helmet monitoring system, which implements a super-resolution (SR) reconstruction driven helmet detection workflow to detect helmets for monitoring tasks. The monitoring system consists of two modules, the super-resolution reconstruction module and the detection module. The former implements the SR algorithm to produce high-resolution images, the latter performs the helmet detection. Validations are performed on both a public dataset as well as the realistic dataset obtained from a practical construction site. The results show that the proposed system achieves a promising performance and surpasses the competing methods. It will be a promising tool for construction monitoring and is easy to be extended to corresponding tasks.

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“…Fang et al [ 19 ] developed a smart non-safety helmet detector on the basis of Faster R-CNN with an accuracy of more than 90% in various scenes, but it takes about 0.2 s to detect an image, which cannot achieve the real-time demand. Gu et al [ 20 ] used multiscale training based on Faster R-CNN and added an anchor strategy to improve it, which eventually led to a 7% improvement in helmet detection accuracy. Due to the shortcomings of two-stage algorithms that cannot meet real-time, one-stage algorithms are increasingly favored by researchers.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Helmet Detection Algorithm Using an Improved YOLOv4

Chen

Deng

Wang

et al. 2023

Sensors

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Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.

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An Advanced Deep Learning Approach for Safety Helmet Wearing Detection

Cited by 16 publications

References 8 publications

Automating Helmet Usage Detection: A YOLOv8 Based Framework

Automating Helmet Usage Detection: A YOLOv8 Based Framework

A Super-Resolution Reconstruction Driven Helmet Detection Workflow

Lightweight Helmet Detection Algorithm Using an Improved YOLOv4

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