Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine tunnels.Although existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective distortion.So,a new safety helmet-wearing detection method based on deep learning is proposed.Firstly,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural network.Secondly,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction part.This block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small objects.Moreover,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance further.Extensive experiments on open dataset validate the proposed method.It reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art method.To be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version 7.The generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

Knowledge, Attitude, and Practice of Using Helmets in Children amongst Parents to Prevent Head Injuries: A Cross-Sectional Study in Riyadh, Saudi Arabia

Objectives: The primary objective was to characterize the range of Knowledge, Attitude, and Practice (KAP) of Helmet use in children amongst parents to prevent head injuries and death. Methods: This is a cross-sectional study, done by online survey using a snowball sampling technique, the number of included responses were 386 parents (Male and female) living in Riyadh Aged 21 - 60 years old or above. Results: The study showed that there is a difference in Parents’ belief in the importance of helmet use while riding a Bicycle vs Motorcycle/Quad bike and that was affected by parents’ education level, almost all the people who answered the survey (76.7%) agree that it is important for their children to wear a helmet when riding both a Bicycle and a Motorcycle or Quadbike with a cumulative percentage of (93.8%). And almost all agreed on multiple approaches to help increase helmet use be it by forcing rental shops to give out helmets, forcing sellers to recommend the use of helmets, increasing awareness campaigns, and imposing fines for not wearing helmets. Conclusions: This study is the first to explore Family helmet use while riding Bicycles and Motorcycles/Quad bikes. Although Parent’s belief in the importance of helmet use for their children was high, it is clear that the level of practice is low. With that the risk of head injuries might be high, our findings suggest that safety interventions for increasing pediatric helmet use are needed to increase helmet use and reduce the risk of head injury and hospitalization.

Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode

The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small objects and objects with obstructions.Therefore,we propose a helmet detection algorithm based on the attention mechanism(AT-YOLO).First of all,a channel attention module is added to the YOLOv3 backbone network,which can adaptively calibrate the channel features of the direction to improve the feature utilization,and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network.Secondly,we use DIoU(Distance Intersection over Union)bounding box regression loss function,it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes,which makes the network more accurate in detecting small objects and faster in convergence.Finally,we explore the training strategy of the network model,which improves network performance without increasing the inference cost.Experiments show that the mAP of the proposed method reaches 96.5%,and the detection speed can reach 27 fps.Compared with other existing methods,it has better performance in detection accuracy and speed.

Modelling ballistic impact on military helmets:The relevance of projectile plasticity

The need to develop armour systems to protect against attacks from various sources is increasingly a matter of personal,social and national security.To develop innovative armour systems it is necessary to monitor developments being made on the type,technology and performance of the threats(weapons,projectiles,explosives,etc.) Specifically,the use of high protection level helmets on the battlefield is essential.The development of evaluation methods that can predict injuries and trauma is therefore of major importance.However,the risk of injuries or trauma that can arise from induced accelerations is an additional consideration.To develop new materials and layouts for helmets it is necessary to study the effects caused by ballistic impacts in the human head on various scenarios.The use of numerical simulation is a fundamental tool in this process.The work here presented focuses on the use of numerical simulation(finite elements analysis) to predict the consequences of bullet impacts on military helmets on human injuries.The main objectives are to assess the level and probability of head trauma using the Head Injury Criterion,caused by the impact of a 9 mm NATO projectile on a PASGT helmet and to quantify the relevance of projectile plasticity on the whole modelling process.The accelerations derived from the impact phenomenon and the deformations caused on the helmet are evaluated using fully three-dimensional models of the helmet,head,neck and projectile.Impact studies are done at impact angles ranging from 0 to 75°.Results are presented and discussed in terms of HIC and probability of acceleration induced trauma levels.Thorough comparison analyses are done using a rigid and a deformable projectile and it is observed that plastic deformation of the projectile is a significant energy dissipation mechanism in the whole impact process.

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

A Slip-Line Method for Calculating Extrusion Force of Steel Helmet with Cold Extrusion Moulding

This paper presents the elastic and plastic deformation of the steel helmet with coldextrusion moulding. The plastic streamline of the plastic mould-making process for ellipse thinplate is described. The distribution of slip-line is established based on the plastic streamline. Theextrusion force of plastic moulding of the steel helmet is calculated by using of slip-line method.Furthermore, an applied example is given.

Helmet-based noninvasive ventilation for acute exacerbation of chronic obstructive pulmonary disease: A case report

BACKGROUND Noninvasive ventilation(NIV)reduces intubation rates,mortalities,and lengths of hospital and intensive care unit stays in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Helmet-based NIV is better tolerated than oronasal mask-based ventilation,and thus,allows NIV to be conducted for prolonged periods at higher pressures with minimal air leaks.CASE SUMMARY A 73-year-old man with a previous diagnosis of COPD stage 4 was admitted to our medical intensive care unit with chief complaints of cough,sputum,and dyspnea of several days’duration.For 10 mo,he had been on oxygen at home by day and had used an oronasal mask-based NIV at night.At intensive care unit admission,he breathed using respiratory accessory muscles.Hypercapnia and signs of infection were detected,and infiltration was observed in the right lower lung field by chest radiography.Thus,we diagnosed AECOPD by communityacquired pneumonia.After admission,respiratory distress steadily deteriorated and invasive mechanical ventilation became necessary.However,the patient refused this option,and thus,we selected helmet-based NIV as a salvage treatment.After 3 d of helmet-based NIV,his consciousness level and hypercapnia recovered to his pre-hospitalization level.CONCLUSION Helmet-based NIV could be considered as a salvage treatment when AECOPD patients refuse invasive mechanical ventilation and oronasal mask-based NIV is ineffective.

Detection of Worker’s Safety Helmet and Mask and Identification of Worker Using Deeplearning

This paper proposes a method for detecting a helmet for thesafety of workers from risk factors and a mask worn indoors and verifying aworker’s identity while wearing a helmet and mask for security. The proposedmethod consists of a part for detecting the worker’s helmet and mask and apart for verifying the worker’s identity. An algorithm for helmet and maskdetection is generated by transfer learning of Yolov5’s s-model and m-model.Both models are trained by changing the learning rate, batch size, and epoch.The model with the best performance is selected as the model for detectingmasks and helmets. At a learning rate of 0.001, a batch size of 32, and anepoch of 200, the s-model showed the best performance with a mAP of0.954, and this was selected as an optimal model. The worker’s identificationalgorithm consists of a facial feature extraction part and a classifier partfor the worker’s identification. The algorithm for facial feature extraction isgenerated by transfer learning of Facenet, and SVMis used as the classifier foridentification. The proposed method makes trained models using two datasets,a masked face dataset with only a masked face, and a mixed face datasetwith both a masked face and an unmasked face. And the model with the bestperformance among the trained models was selected as the optimal model foridentification when using a mask. As a result of the experiment, the model bytransfer learning of Facenet and SVM using a mixed face dataset showed thebest performance. When the optimal model was tested with a mixed dataset,it showed an accuracy of 95.4%. Also, the proposed model was evaluated asdata from 500 images of taking 10 people with a mobile phone. The resultsshowed that the helmet and mask were detected well and identification wasalso good.

Detection of Safety Helmet-Wearing Based on the YOLO_CA Model

Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents,as well as being of great significance to construction safety.However,for a variety of reasons,construction workers nowadays may not strictly enforce the rules of wearing safety helmets.In order to strengthen the safety of construction site,the traditional practice is to manage it through methods such as regular inspections by safety officers,but the cost is high and the effect is poor.With the popularization and application of construction site video monitoring,manual video monitoring has been realized for management,but the monitors need to be on duty at all times,and thus are prone to negligence.Therefore,this study establishes a lightweight model YOLO_CA based on YOLOv5 for the automatic detection of construction workers’helmet wearing,which overcomes the shortcomings of the current manual monitoring methods that are inefficient and expensive.The coordinate attention(CA)addition to the YOLOv5 backbone strengthens detection accuracy in complex scenes by extracting critical information and suppressing non-critical information.Further parameter compression with deeply separable convolution(DWConv).In addition,to improve the feature representation speed,we swap out C3 with a Ghost module,which decreases the floating-point operations needed for feature channel fusion,and CIOU_Loss was substituted with EIOU_Loss to enhance the algorithm’s localization accuracy.Therefore,the original model needs to be improved so as to enhance the detection of safety helmets.The experimental results show that the YOLO_CA model achieves good results in all indicators compared with the mainstream model.Compared with the original model,the mAP value of the optimized model increased by 1.13%,GFLOPs cut down by 17.5%,and there is a 6.84%decrease in the total model parameters,furthermore,the weight size cuts down by 4.26%,FPS increased by 39.58%,and the detection effect and model size of this model can meet the requirements of lightweight embedding.

Calculation and Test of Hitting Force of Firing Bullet on the Bulletproof Helmet

In this paper the hit force of firing bullet on bulletproof helmet has been computed and the test device has been described. The device is divided into two parts: 1) The bullet, helmet and mould are in one system, using moment theorem to calculate the hit force; 2) The mould, sensor and support pole are in one system, using the method in reference [1] that measures the dynamic strain and displacement of simulate target of bulletproof clothes. We compute the transfigure energy and momentum energy when hitting the mould, the work done by the sensor and the expend energy of support pole. We get the hit force of helmet using energy balance principle. The result is according with the test and has been used to design the GGK93T bulletproof helmet and other serial products.

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

Thermoregulatory function and sexual dimorphism of the throat sack in Helmeted Guineafowl(Numida meleagris)across Africa

The responses of ground-dwelling birds to heat and cold stress encompass a variety of behavioural,physiological and even morphological mechanisms.However,the role of glabrous skin in this respect has been marginally addressed so far.The Helmeted Guineafowl(Numida meleagris)is a landfowl distributed across Sub-Saharan Africa with eight traditionally recognised extant subspecies.Among the most prominent morphological traits underlying intraspecific variability are size and pigmentation of the bare throat skin(or sack),which might be related to the different habitats and environmental conditions across its wide range.In order to explore the Helmeted Guineafowl range-wide sack variation and pigmentation in relation to thermoregulation and sexual signalling,we collected morphometric and environmental information for N.m.coronata integrating field data with the inspection of photographic material encompassing seven subspecies and environmental information from their habitats.Field data evidenced that sack size was significantly correlated with ambient temperature,thus pointing to a likely involvement of the throat sack in thermoregulation.When the pictorial data from all subspecies were pooled,sack size correlated negatively with biomass,rainfall and humidity,while a positive correlation was found with annual solar irradiation.Sack size correlated positively with monthly temperature variation among the bluethroated subspecies from southern Africa as opposed to the black-throated subspecies ranging north to Zambia and Mozambique.Still,in this latter group the sack was often larger during winter months,possibly to maximise solar radiation absorbance.Noteworthy,sack size was related to sex dimorphism in two subspecies.Sack morphology and colour in the Helmeted Guineafowl likely modulate body temperature by evaporative cooling or heating upon needs,but in some subspecies it is also seemingly related to sexual signalling.Additional studies are needed to fully understand the multifunctionality of this important morphological feature in this species.

Preventable head and facial injuries by providing free bicycle helmets and education to preschool children in a head start program

The objectives of the study were to determine helmet use rates, incidence rates (IRs) of head and facial injuries for population attributable fraction (PAF) estimation, and to elucidate the magnitude of and changes in PAFs as the result of helmet use changes among preschool children. A study consisting of cross-sectional (survey) and longitudinal (follow-up) component was designed by including a randomly selected group of participants (n = 322) from 10 Head Start sites provided with free bicycle helmets along with a subgroup of prior helmet owners (n = 68) from the other random group (n = 285). All participants received bicycle helmet education. Helmet use surveys were conducted in May (1st Survey) and November 2008 (2nd Survey). The helmet owners were followed up to determine IRs, and incidence rate ratios (IRRs) for head and facial injuries. PAFs were computed using IRs as well as helmet use rates and IRRs. Helmet use rates increased significantly from the 1st to the 2nd Survey. The mean follow-up person-time was 5 months. The IRs for head, face (all portions), and face (upper/mid portions) injuries were higher in non-helmeted than helmeted riders. By using IRs, PAFs for the 3 injuries among the riders in both groups of helmet owners were 77%, 22%, and 32% respectively. The PAFs for each of the above injuries decreased by about 10% as helmet use rates increased. The magnitude of and changes in preventable head and facial injuries following free bicycle helmet distribution and education among helmeted riders was elucidated in this Head Start preschool children population.

An Examination of Concussion Injury Rates in Various Models of Football Helmets in NCAA Football Athletes

While newer, advanced helmet models have been designed with the intentions of decreasing concussions, very little research exists on injury rates in various football helmets at the collegiate level. The aim of this study was to examine concussion injury rates in various models of football helmets in collegiate football athletes. In addition, to compare injury rates of newer, advanced football helmets to older, traditional helmets among collegiate football athletes, a total of 209 concussions and 563,701 AEs （athlete-exposures） among 2,107 collegiate football athletes in seven helmet models were included in the analyses. Concussion injury rates revealed that the Riddell Revolution~ had the highest rate of 0.41 concussions per 1,000 AEs. The Schutt ION 4DTM helmet had the lowest rate of 0.25 concussions per 1,000 AEs. These newer helmet models did not significantly differ from one another （P = 0.74）, however, all models significantly differed from the older, traditional helmet model （P 〈 0.001）. The findings of this study suggest that concussion rates do not differ between newer and more advanced helmet models. More importantly, there are currently no helmets available to prevent concussions from occurring in football athletes.

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.

Worker’s Helmet Recognition and Identity Recognition Based on Deep Learning

For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brings trouble to the subsequent safety education of workers. Although, many scholars have devoted themselves to the study of person re-identification which neglected safety detection. The study of this paper mainly proposes a method based on deep learning, which is different from the previous study of helmet detection and human identity recognition and can carry out helmet detection and identity recognition for construction workers. This paper proposes a computer vision-based worker identity recognition and helmet recognition method. We collected 3000 real-name channel images and constructed a neural network based on the You Only Look Once (YOLO) v3 model to extract the features of the construction worker’s face and helmet, respectively. Experiments show that the method has a high recognition accuracy rate, fast recognition speed, accurate recognition of workers and helmet detection, and solves the problem of poor supervision of real-name channels.

A method for detecting miners based on helmets detection in underground coal mine videos

In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.

基于改进YOLOv5的复杂场景电动车头盔检测方法

佩戴电动车头盔是安全骑行的重要保障,对电动车驾乘人员佩戴头盔进行有效检测在保障驾乘人员安全方面具有重要意义。电动车头盔检测中存在目标之间相互遮挡、复杂背景干扰、头盔目标小等问题,现有方法尚不能满足复杂场景下电动车头盔检测的要求,因此,提出一种改进YOLOv5的复杂场景电动车头盔识别方法。首先,提出一种新的主干网络结构ML-CSPDarknet53,增强网络的特征提取能力,引入轻量级上采样算子CARAFE,利用特征图语义信息扩大感受野;其次,搭建坐标卷积CoordConv模块,增强网络对空间信息的感知能力,并将WIoU v3作为边界框损失函数,降低低质量样本对模型性能的不利影响;最后,构建了内容丰富的头盔检测数据集对改进算法进行验证。实验结果表明,改进后算法相较于原算法在精确度、召回率、mAP@0.5、mAP@0.5:0.95上分别提升了2.9%、3.0%、3.4%和2.2%,并且性能优于其他主流检测算法,满足复杂道路交通场景下电动车驾乘人员头盔检测的任务要求。

Influencing factors analysis of helmet wearing for electric bicycle riders based on ordinal multinomial logistic model

The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influencing factors of electric bicycle helmet wearing.First,living area,gender,age,marital status and education level are selected as independent variables for data analysis.The factor analysis divides the sentiments of electric bicyclists for wearing helmets into four aspects:safety perception,practical sensation,satisfaction perception and emergency perception,and ordinal multiple logistic models are built to analyse the influencing factors.The result shows that people aged 18−25,26−35,36−45 and 46−55 are 1.3,1.8,2.0 and 2.3 times more likely,respectively,to have at least a grade higher safety perception than those aged 56 and over;men are 0.77 times more likely than women to feel at least one grade higher about the practical perception and 1.48 times more than women about the satisfaction perception;people with primary school,junior high school,senior high school,junior college and bachelor’s degree education are 1.64,2.44,1.50,1.70 and 1.55 times more likely,respectively,than people with a master’s degree to feel at least one grade higher about the satisfaction perception.