Wearing Safety Helmets Research Articles

Causes and Trends of Motorcycle Accidents

Road safety has long been considered as one ofthe social responsibilities to the Malaysian Government. Inthe visibility of this responsibilities, multiple bodies concernon road safety has been formed within the governmentdepartments, private agencies, and voluntary organizations.The current study was carried out to understand the recenttrend and statistics of road motorcycles in Kajang,Malaysia. The statistics for road accidents and the relatedcasualties in Malaysia from 2005 to 2008 was obtained fromthe Bukit Aman Police Headquarters. The statistics ofregistered motor vehicles in Malaysia will be provided byRoad Transport Department of Malaysia. Data will beanalyzed using SPSS software. Descriptive and hypothesistesting techniques were used to obtain the results based onthe stated objectives of the study. From the crash statistics,it can be revealed that Kajang has a relatively higherincidence of casualties (dead, seriously injured and lightlyinjured). In addition, the results show that the young ridersof motorcycles among the age of 16 to 20 years from themajority of those who die in motorcycles accidents.Moreover, it is recognized that the considerable percentageof the motorcyclists and the pillion riders did not wearsafety helmets when they were traveling on the roadsnetwork. The Malaysian government has to organize manymotorcycle safety campaigns in the coming few years.

Analisis Tingkat Resiko Kecelakaan Kerja pada Galangan Kapal Logistik di PT X dengan Metode HIRARC

Logistics companies are very important in managing materials for smooth operations. In this company, which specializes in logistics through ocean shipping, faces risks when carrying out tasks such as lifting logistics goods in containers, moving oil, coal, and machine tools. To minimize the possibility of accidents, is necessary to identify, analyze, and manage potential hazards using the HIRARC method. Risk assessment shows variations in risk levels for different activities and control efforts. Hazard control is carried out through measures such as being exposed to embers that cause blisters, which can be prevented by controlling them using complete personal protective equipment, falling from stairs can be prevented by routine inspection of the condition of the stairs, triggering fires in the combustion chamber can be prevented by providing training to oil pump operators, bumping heads can be prevented by wearing safety helmets, and Being crushed by goods can be prevented by neat arrangement of goods.

Helmet wearing detection algorithm based on improved YOLOv5

In industrial production, workers need to wear safety helmets at all times. However, due to different lighting, viewing angles, and the tendency of people to block each other, the precision of target detection is not high enough. Aiming at this problem, a real-time detection of helmets was achieved by improving the YOLOv5 algorithm. This algorithm introduces the lightweight network structure FasterNet, which uses partial convolution as the main operator to reduce the amount of calculations and parameters of the network; the boundary regression loss function Wise-IoU loss function with a dynamic focusing mechanism replaces the original loss function in YOLOv5; finally, the CBAM attention mechanism is introduced to obtain global context information and improve the detection ability of small targets. The experimental results show that the parameters of the improved YOLOv5 model are reduced by 12.68%, the computational amount is reduced by 10.8%, the mAP is increased from 88.3 to 92.3%, and the inference time is reduced by 81.5%, which is better than the performance of the original model and can detect helmet wearing effectively and in real time.

A comprehensive review on helmet detection and number plate recognition approaches

Motorbikes serve as the primary mode of transportation in most countries as they are cost effective and appropriate for a nuclear family. But it has been observed that more than 70% of the users do not prefer to wear safety helmets for various reasons jeopardizing their lives and falling prey to accidents. The most prevalent method for ensuring this right now is traffic police manually monitoring the motorcyclists. But due to excess traffic and limited traffic personnels, many violators go unrecognized and continue to practice the same. Thus, it is important to eliminate the human intervention and automate the monitoring system using deep learning and computer vision-based techniques. Our proposed system implements this by extracting number plate of helmet violators and generates an e-challan on the registered mobile number. We propose using a custom trained YOLO-v8 model for violation detection and YOLO-v8 + EasyOCR for number plate detection and extraction. Canny Edge Detection is a preprocessing step that can be used to enhance the edges of objects in an image, making them more distinguishable. This system holds great potential for enhancing safety-related policies and ensuring strict enforcement of traffic regulations. Additionally, it contributes to the advancement of traffic management through the implementation of an AI-based automated traffic violation and ticketing system.

Research on improved algorithm for helmet detection based on YOLOv5

The continuous development of smart industrial parks has imposed increasingly stringent requirements on safety helmet detection in environments such as factories, construction sites, rail transit, and fire protection. Current models often suffer from issues like false alarms or missed detections, especially when dealing with small and densely packed targets. This study aims to enhance the YOLOv5 target detection method to provide real-time alerts for individuals not wearing safety helmets in complex scenarios. Our approach involves incorporating the ECA channel attention mechanism into the YOLOv5 backbone network, allowing for efficient feature extraction while reducing computational load. We adopt a weighted bi-directional feature pyramid network structure (BiFPN) to facilitate effective feature fusion and cross-scale information transmission. Additionally, the introduction of a decoupling head in YOLOv5 improves detection performance and convergence rate. The experimental results demonstrate a substantial improvement in the YOLOv5 model's performance. The enhanced YOLOv5 model achieved an average accuracy of 95.9% on a custom-made helmet dataset, a 3.0 percentage point increase compared to the original YOLOv5 model. This study holds significant implications for enhancing the accuracy and robustness of helmet-wearing detection in various settings.

Construction Instance Segmentation (CIS) Dataset for Deep Learning-Based Computer Vision

Deep learning-based computer vision (DLBCV) techniques have played an important role in intelligent construction. Image datasets are essential for developing DLBCV algorithms. However, a large-scale construction-specific dataset of major construction elements, such as precast components (PCs), PC delivery trucks, and workers not wearing safety helmets, remains absent. This paper presents the Construction Instance Segmentation (CIS) dataset, a new image dataset aimed at advancing state-of-the-art instance segmentation in the field of construction management. It contains 50,000 images with ten object categories belonging to construction workers, machines, and materials. Two rounds of algorithmic analysis have been conducted to refine and balance the dataset. Finally, a detailed statistical analysis of the dataset is presented.

Recognition of Safety Helmet Wearing Of Operator Local Ground Floor Unit 2 Suralaya PGU Based On Improved YOLOv3

PT. PLN Indonesia Power Suralaya Power Generation Unit requires its operators on the local ground floor to wear safety helmets to prevent head injuries while performing their tasks. Observation of workers wearing safety helmet must be ensured by the company to prevent head injury by operators. The Culture Transformation Program of K3 PT PLN Indonesia Power brings a new observation method to detect safety helmet wearing of operator local ground floor using real-time detection. YOLOv3 is an application which can be used to real-time detection helmet wearing of workers using the darknet53 algorithm based on YOLOv1 and YOLOv2 by using data images that have been collected. Based on YOLOv3 model, the improved version of YOLOv3 is proposed to improve accuracy and speed detection of safety helmet wearing by combining multi-scale detection training. Different YOLOv3 versions will be used to compare results of helmet recognition. The improved YOLOv3 show results 96.63% mAP50 and 725,711 milliseconds better than the other versions of YOLO to detect helmet safety. The experimental result show that the improved YOLOv3 have satisfying with the detection speed and accuracy of safety helmet wearing detection by operators at PT PLN Indonesia Power Suralaya Power Generation Unit.

The effect of safety attire on perceptions of cyclist dehumanisation

Increasing the uptake of active, carbon neutral forms of transport is indicated for both population health and environmental conservation. Efforts to increase cycling uptake are hindered by negative attitudes towards cyclists. Recent research from Australia has found that many people consider cyclists to be less than fully human. There is currently a lack of empirical evidence that explains these dehumanising perceptions. Most people who ride bicycles in Australia wear safety helmets as required by mandatory helmet laws. We hypothesised that people wearing bicycle helmets are perceived as less human compared to people without helmets due to reduced visibility of eyes and hair. We tested this hypothesis through a survey (n = 563) comprised of two-paired alternate forced choice questions to identify which image of a cyclist respondents consider to be less human. We then analysed the results using a Bradley-Terry probability model. We found images of cyclists wearing helmets or safety vests to have a higher probability of being selected as less human compared to images of cyclists wearing no safety equipment. The results have implications for research on cyclist dehumanisation and its mitigation.

Application Effect Analysis for Helmet Detection Algorithm based on YOLOV5

Safety helmets can provide better safety protection for the human head in industrial production, which is one of the important protective tools to protect the lives of workers. In the on-site environment, checking whether construction workers are wearing safety helmets mainly depends on manual inspection, which is time-consuming and labor-intensive. In order to solve the problem of hard hat detection and recognition in the complex environment of the construction site, in this paper, we propose a YOLOV5 hard hat detection method. Specifically, a pre-trained object model is used to locate the area of the worker and the face detection module is used to mark the face. Then, the helmet recognition module is used to extract the sub area of the worker's head. Finally, the extracted image is classified into two categories to judge the situation of the worker wearing the helmet. Focusing only on workers can reduce training time and improve the efficiency of the model, so the training label only uses the label of people. The model refines and extracts the head, inputs the separated head area into the subsequent network for subsequent analysis, and then uses the binary classification method to judge whether the helmet is worn. We compare the results of different models such as YOLOV5s, YOLOV5m and YOLOV5l, where YOLOV5l can achieve a best mAP of 91.9%. We also analyze the effect of light to the performance, where the detection effect will be affected by low visibility through the comparative detection of samples in the day and at night.

Helmet-Wearing Tracking Detection Based on StrongSORT.

Object detection based on deep learning is one of the most important and fundamental tasks of computer vision. High-performance detection algorithms have been widely used in many practical fields. For the management of workers wearing helmets in construction scenarios, this paper proposes a framework model based on the YOLOv5 detection algorithm, combined with multi-object tracking algorithms, to monitor and track whether workers wear safety helmets in real-time video. The improved StrongSORT tracking algorithm of DeepSORT is selected to reduce the loss of the tracked object caused by the occlusion, trajectory blur, and motion scale of the object. The safety helmet dataset is trained with YOLOv5s, and the best result of training is used as the weight model in the StrongSORT tracking algorithm. The experimental results show that the mAP@0.5 of all classes in the YOLOv5s model can reach 95.1% in the validation dataset, mAP@0.5:0.95 is 62.1%, and the precision of wearing helmet is 95.7%. After the box regression loss function was changed from CIOU to Focal-EIOU, the mAP@0.5 increased to 95.4%, mAP@0.5:0.95 increased to 62.9%, and the precision of wearing helmet increased to 96.5%, which were increased by 0.3%, 0.8% and 0.8%, respectively. StrongSORT can update object trajectories in video frames at a speed of 0.05 s per frame. Based on the improved YOLOv5s combined with the StrongSORT tracking algorithm, the helmet-wearing tracking detection can achieve better performance.

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning.

Safety Helmet Wearing Detection Model Based on Improved YOLO-M

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.

MCA-YOLOV5-Light: A Faster, Stronger and Lighter Algorithm for Helmet-Wearing Detection

It is an essential measure for workers to wear safety helmets when entering the construction site to prevent head injuries caused by object collision and falling. This paper proposes a lightweight algorithm for helmet-wearing detection based on YOLOV5, which is faster and more robust for helmet detection in natural construction scenarios. In this paper, the MCA attention mechanism is embedded in the backbone network to help the network extract more productive information, reduce the missed detection rate of small helmet objects and improve detection accuracy. In order to ensure the safety of workers in construction, it is necessary to detect whether the construction workers are wearing safety helmets in real-time to achieve monitoring on-site. A channel pruning strategy is proposed on the MCA-YOLOv5 algorithm to compress it, realizing the optimal large-scale model into ultrasmall models for real-time detection on embedded or mobile devices. The experimental results on the public data set show that the model parameter volume is reduced by 87.2%, and the detection speed is increased by 53.5%, even though the MCA-YOLOv5-light reduces the mAP slightly.

Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety

Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions.

Multi-Scale Safety Helmet Detection Based on RSSE-YOLOv3.

Due to the shielding of dense and small targets, real-time detection of whether construction workers are wearing safety helmets suffers from low detection accuracy and missed detection. In this paper, a new detection algorithm based on YOLOv3 is proposed. Firstly, the parallel network RepVGG Skip Squeeze Excitation (RSSE) module is used to replace the Res8 module in the original YOLOv3 network. The RSSE module consists of 3 × 3 convolutional fusion channels and SSE branches fused. The introduction of the R-SSE module increases the network width, reduces the network depth, and improves the network detection speed and accuracy. Secondly, to avoid gradient disappearance and improve feature reuse, the residual module Res2 is used to replace the CBL×5 modules. Finally, the resolution of the input image is improved, and the four-scale feature prediction is used instead of the three-scale feature prediction to further improve the efficiency of detecting small targets. This paper also introduces the complete joint crossover (CIOU) to improve the loss function and positioning accuracy. The experimental results show that, compared with the original YOLOv3 algorithm, the improved algorithm improves the precision (P) by 3.9%, the recall (R) by 5.2%, and the average precision (mAP) by 4.7%, which significantly improves the performance of the detection.

An Unsafe Behavior Detection Method Based on Improved YOLO Framework

In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%.

Research on the Application of Helmet Detection Based on YOLOv4

Helmets are one of the important measures to ensure the safety of construction workers. Because the harm caused by not wearing safety helmets as required is great, the wearing of safety helmets has also attracted more and more people’s attention. At present, the main method of helmet detection is the YOLO series of algorithms. They often only focus on detection accuracy, ignoring the actual situation during deployment, that is, a balance between accuracy and speed is required. Therefore, this paper proposes a helmet detection application based on YOLOv4 algorithm, and combined with the MobileNet network, it has achieved good results in terms of detection accuracy and speed. Through transfer learning and tuning parameters, the mAP and FPS values detected in this paper on the public safety helmet datasets are 94.47% and 27.36%, which exceed the research work of some similar papers. This paper also combines YOLOv4 and MobileNetv3 networks to propose a mobileNet-based YOLOv4 helmet detection application. Its mAP and FPS values are 91.47% and 42.58%, respectively, which meet the accuracy and real-time requirements of current hardware deployment.

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Wearing safety helmets can effectively protect workers safety on construction sites. However, workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector with high speed and accuracy is urgently needed. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize. Therefore, this paper proposes a deep learning-based method to detect safety helmet wearing at a satisfactory accuracy with high detection speed. Our method chooses YOLO v5 as the baseline, then the fourth detection scale is added to predict more bounding boxes for small objects and the attention mechanism is adopted in the backbone of the network to construct more informative features for following concatenation operations. In order to overcome the defects caused by insufficient data, targeted data augmentation and transfer learning are used. Improvements caused by every modification are discussed in this paper. Finally, our model achieves 92.2&#x0025; mean average precision, up 6.3&#x0025; compared to the original algorithm, and it only takes 3.0 ms to detect an image at <inline-formula> <tex-math notation="LaTeX">$640 \times 640$ </tex-math></inline-formula>. These results demonstrate the robustness and feasibility of our model. Meanwhile, the size of our trained model is only 16.3 m, which means the model is easy to be deployed. At last, after obtaining a satisfactory model, a graphical user interface (GUI) is designed to make our algorithm more user-friendly.

Safety Helmet Wearing Detection in Aerial Images Using Improved YOLOv4

In construction, it is important to check whether workers wear safety helmets in real time. We proposed using an unmanned aerial vehicle (UAV) to monitor construction workers in real time. As the small target of aerial photography poses challenges to safety-helmet-wearing detection, we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography: (1) By increasing the dimension of the effective feature layer of the backbone network, the model's receptive field is reduced, and the utilization rate of fine-grained features is improved. (2) By introducing the cross stage partial (CSP) structure into path aggregation network (PANet), the calculation amount of the model is reduced, and the aggregation efficiency of effective features at different scales is improved. (3) The complexity of the YOLOv4 model is reduced by introducing group convolution and the pruning PANet multi-scale detection mode for de-redundancy. Experimental results show that the improved YOLOv4 model achieved the highest performance in the UAV helmet detection task, that the mean average precision (mAP) increased from 83.67% of the original YOLOv4 model to 91.03%, and that the parameter amount of the model is reduced by 24.7%. The results prove that the improved YOLOv4 model can effectively respond to the requirements of real-time detection of helmet wearing by UAV aerial photography.

Detection and Tracking of Safety Helmet in Construction Site

In the process of modern industrial production, the safety helmet worn by workers on the construction site is of great significance to their own safety protection. Aiming at the situation of workers wearing safety helmets on the construction site, this paper proposes a method of target depth detection based on the vibe algorithm based on HSV color extraction and target tracking by Kalman filter integrating centroid contour. First, the HSV color of the helmet was extracted according to the video monitoring screen, and then the vibe algorithm was used to segment the foreground target contour, and finally the center of mass and contour were fused to carry out Kalman helmet tracking. Experimental results show that this method can accurately identify and track the safety helmet and effectively protect the personal safety of workers.