Fast helmet-wearing-condition detection based on improved YOLOv<sub>2</sub>

Ming, 方明 Fang; Teng-teng, 孙腾腾 Sun; Zhen, 邵 桢 Shao

doi:10.3788/ope.20192705.1196

Cited by 27 publications

(5 citation statements)

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“…The two onedimensional vectors after pooling are sent to the fully connected layer. Finally, the channel attention 𝑍 𝑐 is generated through element-wise summation and sigmoid activation function, which is shown in (5).…”

Section: B Improvements Of Attention Mechanismmentioning

confidence: 99%

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Safety Helmet Wearing Detection Model Based on Improved YOLO-M

2023

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In recent years, construction accidents have occurred frequently. The safety guarantee measures for construction personnel are thrown into sharp focus. Wearing helmets is one of the most important requirements to protect the safety of construction personnel, and the detection of wearing safety helmets has become necessary. For the problems of the existing helmet wearing detection algorithm such as too many parameters, substantial detection interferences, and low detection accuracy, in this paper a helmet wearing detection model YOLO-M is proposed. Firstly, MobileNetv3 is adopted as the backbone network of YOLOv5s for the feature extraction, which can reduce the number of model parameters and model size. Secondly, a residual edge is introduced in the feature fusion. The original feature map information is fused during feature fusion, and the detection ability of small targets is enhanced. At last, by changing the connection between CAM and SAM, a new attention module BiCAM is designed. The comparison experiments show that the detection accuracy of YOLO-M is 2.22% higher than YOLOv5s, and the model parameter quantity is reduced to 3/4 of YOLOv5s. Under the same detection conditions, the detection speed of YOLO-M is better than the other models, which meets the accuracy requirements of helmet detection in the construction scene.INDEX TERMS Attention mechanism, feature fusion, safety helmet, YOLOv5s model.

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Section: B Improvements Of Attention Mechanismmentioning

confidence: 99%

“…Furthermore, a new objective function was proposed, which improved the accuracy [4]. An algorithm applied the lightweight structure on the MobileNet network and it compressed YOLOv2 [5]. YOLO-S was a lightweight helmet wearing detection model.…”

Section: Introductionmentioning

confidence: 99%

Safety Helmet Wearing Detection Model Based on Improved YOLO-M

2023

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“…Currently, in the research of helmet detection algorithms, most projects use deep learning methods to directly detect. For example, some researchers directly use Faster-RCNN for detection [7], or by improving the existing algorithm YOLO for detection [1] [9]. Some algorithms achieve detection by first detecting pedestrians, and then positioning to the human head area [8], but they can't position the safety helmet accurately.…”

Section: Helmet Detection Algorithmmentioning

confidence: 99%

Safety helmet detection based on face detection and regression

Huang

Zhu

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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Wearing a safety helmet can effectively reduce or prevent injury to the worker's head caused by hazardous materials in the construction site. However, due to poor supervision, safety accidents often occur when workers don't wear safety helmets. In this paper, we propose a safety helmet detection algorithm based on face detection and ridge regression. Firstly, we get the location information of the face box and the five key points of the face through face detection algorithm, and then get the helmet detection box corresponding to face through ridge regression model. We collected 4000 images of people wearing helmets for training and testing of ridge regression models. Compared with some of the most advanced methods, we have achieved very good results in the test set. The results show that mIoU reaches 70.118% and the detection rate is improved.

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“…Hui Shi et al [15] trained a multi-scale structure in YOLOv3 by adding feature pyramids to get the feature layers at different scales, which in turn are used to predict the locations of workers and helmets. Ming F et al [16] integrated the dense connectivity method into the YOLOv2 model to accomplish the helmet detection task. Minyu Wu et al [17] added an inverse convolution module to enhance the model's expressiveness for the small target of helmets.…”

Section: Introductionmentioning

confidence: 99%

Cutmix-Plus: an improved helmet wear detection data enhancement technique

Jiang,

Lu,

Chen

2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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Fast helmet-wearing-condition detection based on improved YOLOv₂

Cited by 27 publications

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Safety Helmet Wearing Detection Model Based on Improved YOLO-M

Safety Helmet Wearing Detection Model Based on Improved YOLO-M

Safety helmet detection based on face detection and regression

Cutmix-Plus: an improved helmet wear detection data enhancement technique

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Fast helmet-wearing-condition detection based on improved YOLOv2

Cited by 27 publications

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Safety Helmet Wearing Detection Model Based on Improved YOLO-M

Safety Helmet Wearing Detection Model Based on Improved YOLO-M

Safety helmet detection based on face detection and regression

Cutmix-Plus: an improved helmet wear detection data enhancement technique

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Fast helmet-wearing-condition detection based on improved YOLOv₂