Search Results (184)

The high number of deaths and serious injuries in traffic crashes can be considered a silent global epidemic, as it is still understood by part of society as an inherent consequence of road traffic. There are several risk factors that can increase the occurrence or severity of crashes on roads, acting alone or in combination. Road safety diagnoses based on facts and evidence are essential for improving public policies to reduce victims. With the aim of assisting in these diagnoses and since the official database on these victims is not made available in detail to the public, this work investigates the relationship between seven indicators, collected in field research and in public databases, and the occurrence and fatality of traffic victims in the City of Rio de Janeiro. Linear regression models are developed for each approach and the one with the best statistical parameters is chosen. The model with greater robustness demonstrated that helmet non-use, the density of traffic enforcement cameras, and illiteracy together explain a significant portion of the variation in the fatality rate. The results are considered satisfactory, since a limited number of existing risk factors for road safety were used. Full article

This study investigated the feasibility of a wearable wind energy-harvesting system integrated into a motorcycle helmet that converts riding-induced airflow into storable electrical energy. A compact horizontal-axis turbine-based system was designed and optimized through staged experiments focusing on generator selection, housing geometry, rotor configuration, and circuit-connected performance. A medium-scale generator, diffuser-type housing (Hd), and eight-blade pinwheel rotor (Rb) were identified as the most suitable combination for helmet-scale integration. The final prototype incorporated two side-mounted turbine modules, a crown-mounted harvesting–boost circuit, and a detachable rechargeable battery pack within a full-face helmet platform. In a field-based riding experiment, the prototype produced mean outputs of 3.99 V, 39.51 mA, and 157.64 mW at 30 km/h; 4.43 V, 43.48 mA, and 192.61 mW at 40 km/h; and 5.45 V, 53.53 mA, and 291.73 mW at 50 km/h. A static wearability evaluation with six participants indicated no obvious discomfort under a quasi-riding posture. These findings support the practical feasibility of helmet-integrated wind energy harvesting as an auxiliary power source for low-power wearable electronics, while highlighting the need for future studies on aerodynamic validation, dynamic wearability, acoustic burden, and safety-oriented structural refinement. Full article

We developed a Python-integrated closed-loop industrial safety system that bridges real-time helmet-compliance detection with immediate machine control. The custom Python application serves as critical middleware, orchestrating the complete pipeline from You Only Look Once Version 8 computer vision inference to industrial automation by trans-lating AI detection results into Object Linking and Embedding for Process Control Unified Architecture communications with a Mitsubishi programmable logic controller (PLC). The Python framework implements configurable safety policies through polygonal zones with authorized helmet colors, incorporates persistence filtering to prevent nuisance trips, and ensures deterministic translation from probabilistic AI outputs to Boolean PLC con-trol signals. Validation demonstrates reliable, low-latency safety actuation with clear ar-chitectural separation between vision processing, Python-mediated policy enforcement, and PLC-based deterministic control. Full article

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

The proper wearing of safety helmets is critical for worker safety in high-risk construction environments, with the fastening of the chin strap serving as a key indicator of correct usage. However, existing detection methods primarily focus on identifying helmet presence, neglecting the crucial assessment of chin strap compliance. This paper proposes an intelligent detection approach that integrates YOLOv8 object detection, instance segmentation, and skin tone recognition to evaluate chin strap wearing status. The system first employs YOLOv8 to detect workers and helmets, filtering out non-wearers before performing facial and neck region segmentation, thereby concentrating computational resources on compliance verification. To address challenges in distinguishing chin straps from similar skin tones under complex lighting conditions, the method incorporates illumination compensation and YCbCr-based skin segmentation. Finally, strap status is determined through morphological operations and contour analysis, with visual annotation of the detection results. This study utilizes a dataset comprising 2000 safety helmet images, which was partitioned into training, validation, and test sets in an 8:1:1 ratio for model training and evaluation. The experimental results demonstrate that the proposed method achieves an accuracy of 96% in detecting chin strap status, exhibits robust performance across diverse construction site conditions, and holds significant practical value and application potential. Full article

The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, they do not consider the most important aspect of the emergency response, which is the Golden Hour the time frame during which medical intervention can have the most significant impact. This paper is a development and validation of an autonomous, low-cost smart helmet architecture that is programmed to operate in real-time to detect accidents and autonomously inform the operator of accidents. The system is built up of an ESP32 microcontroller with a multi-modal sensor package, which comprises an inertial measurement unit (IMU), force-impact sensors, and MQ-3 alcohol sensors to conduct proactive safety screening. To overcome the single threshold limitation of unreliable systems, a time-windowed sensor-fusion algorithm was applied in order to distinguish between normal riding dynamics and bona fide collisions. This reasoning involves concurrent cues of high-G inertial rotations and physical impacting features over a time window of 500 ms to reduce spurious activations. The architecture of the system is completely self-sufficient and employs an in-built GPS-GSM module to send the geographical location through SMS without the need to have a smartphone connection. The prototype was also put through 150 experimental tests, with some conducted in laboratories, and real-world running tests in diverse terrains. The findings reveal an accuracy in detection of 93.7, a false positive rate (FPR) of 2.6 and a mean emergency alert latency of 2.8 s. In addition, it was found that structural integrity was confirmed at ECE 22.05 impact conditions using Finite Element Analysis (FEA), with a safety factor of 1.38. These quantitative results mean that the proposed system is an effective way to address a cultural shift between passive structural protection and active rescue intervention as a statistical and computationally efficient safety measure of modern micro-mobility. Full article

Construction site safety remains a critical global challenge, demanding urgent attention. Existing surveillance systems struggle to balance multi-object detection accuracy, real-time efficiency, and environmental robustness under strict edge constraints. This paper presents MMDet-Edge, an edge-optimized unified detection framework that addresses these competing demands via three synergistic innovations. First, an adaptive feature fusion architecture with a learnable spatial–channel attention mechanism resolves cross-scale conflicts, boosting small-object average precision (AP) by 9.3%. Second, a hardware-conscious neural architecture search (HC-NAS) strategy co-optimizes sparsity patterns and quantization sensitivity, achieving a state-of-the-art performance of 89.4% mAP@0.5 at only 1.8 W power consumption—surpassing contemporary edge detectors by 6.3% mAP under equivalent power budgets. Third, by incorporating OSHA fatality statistics into a novel risk-weighted evaluation paradigm, we reduce high-consequence false negatives by 34%. Comprehensive evaluations on a purpose-built benchmark and cross-dataset tests demonstrate MMDet-Edge’s superiority. It outperforms a wide range of state-of-the-art models. Validated across three active construction sites, the system enables real-time detection of five safety-critical targets (personnel, helmets, flames, smoke, vests) under extreme conditions, including >60% occlusion and >100 lux illumination variance. Our field deployments demonstrated a 22% reduction in safety incidents compared to conventional systems, establishing a new architectural paradigm for safety-critical edge AI through principled hardware–algorithm co-design. Full article

Motorcycle use is rising in low- and middle-income countries, leading to more crashes. Many deaths from these crashes are preventable with correct helmet use. This study examined correct helmet use trends and factors influencing it. A roadside cross-sectional observational study was conducted in 10 randomly selected locations across 10 sub-cities of Addis Ababa from 2015 to 2020 twice a year. Binary logistic regression analysis was performed to identify predictors of correct helmet use. Out of 39,246 drivers and 12,950 passengers observed, 75% of drivers and 26.2% of passengers wore helmets. However, according to the Ethiopian helmet law (which requires the strapped use of any helmet type), only 34.2% of observed drivers (n = 39,246) and 9.1% of observed passengers (n = 12,950) wore helmets correctly. Under the global best-practice standard (strapped use of approved helmets excluding cap helmets) was even lower at 29.6% among drivers and 6.6% among passengers. Correct use declined over six years until the 2019 reinitiation of helmet law enforcement. Among drivers, correct use was linked to full-face helmets (AOR = 1. 90, 95% CI: 1.77–2.04), police enforcement (AOR = 1.08, 95%CI: 1.02–1.14), rain (AOR = 1.26, 95% CI: 1.14–1.40), and riding on arterial roads (AOR = 1.89, 95% CI: 1.78–2.00). For passengers, being female (AOR = 1.55, 95% CI: 1.09–2.19), aged ≥18 (AOR = 2.16, 95% CI: 1.34–3.46), and riding with correctly helmeted drivers (AOR = 3.37, 95% CI: 2.88–3.95) increased correct use. The findings indicate a need for a combination of interventions, including awareness-raising campaigns, sustained enforcement, and preparing helmet standards. Full article

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article

Background: Since the introduction of rental e-scooters, they have become a popular mode of transportation not only in German cities but in other cities as well. However, this rapid increase in usage has coincided with a significant rise in associated injuries and accidents, outpacing those related to bicycles. A disproportionate number of these incidents involve alcohol consumption and young people under the age of 25, with a low incidence of helmet use. Following the example of driver training for children on bicycles, we carried out driver safety training with e-scooters and examined the results scientifically. Methods: The study conducted three voluntary driving safety training sessions in Berlin and Munich, with participants completing questionnaires before and after the training to measure their knowledge and skills (on a scale between 0 and 5; 0 = totally insecure and 5 = absolutely secure). The training included a technical introduction, practical exercises, and an educational component on injury data and prevention strategies. During the statistical analysis, the novice drivers (group 1) were compared to the non-novice drivers (group 2). Results: Out of 136 participants, 103 completed the training (a response rate of 75.7%). The mean age of the participants was 37.1 years, and 52.4% of them were female. A total of 59% had never used an e-scooter and were therefore assigned to group 1 (group 2 = experienced drivers). Both groups showed significant improvements in both knowledge of traffic laws and driving skills. Conclusions: The findings suggest that driving safety training potentially enhances the safe operation of e-scooters. However, the training demands a high level of time and motivation, making it less attractive for younger drivers who are most prone to accidents. Therefore, we recommend the use of digital driving safety training before the first use of e-scooters. Full article

Reliable detection of safety helmets is essential for ensuring personnel protection in large-scale outdoor operations. However, recognition becomes difficult when monitoring relies on low-resolution or compressed video streams captured by fixed or mobile platforms such as UAVs—conditions commonly encountered in intelligent transportation and urban surveillance. This study proposes a super-resolution-enhanced detection framework that integrates video super-resolution with ROI-guided inference to improve the visibility of small targets while reducing computational cost. Focusing on a single, carefully selected VSR module (BasicVSR++), the framework achieves an F1-score of 0.904 in helmet detection across multiple low-quality surveillance scenarios. This demonstrates the framework’s effectiveness for robust helmet monitoring in low-resolution and compressed surveillance scenarios. Full article

The construction industry relies on the Internet of Things (IoT) and wearable technologies to enhance workplace safety. This research investigates the use of IoT and wearable technology among Saudi Arabian construction sector employees, analyzing their implementation difficulties and the factors contributing to successful implementation. A structured questionnaire was distributed to 567 construction professionals across different roles and projects. Frequency analysis was used to study adoption patterns, chi-square tests to study demographic factors, and principal component analysis for exploratory factor analysis to discover hidden adoption factors. The findings show that smart safety vests and helmets receive the highest level of recognition. On the other hand, advanced monitoring systems, including fatigue and environmental sensors, are not used enough. Group differences in device adoption were investigated in terms of years of experience, academic qualification, job role, and project budget. The findings from factor analysis show that three main factors determine adoption rates, which include (1) safety and operational effectiveness, (2) worker acceptance and support structures, and (3) technical and adoption barriers. A data-driven system is created to help policymakers and industry leaders accelerate construction safety digitalization efforts. Full article

Safety on construction sites is an essential yet challenging issue due to the inherently hazardous nature of these sites. Workers are expected to wear Personal Protective Equipment (PPE), such as helmets, vests, and safety glasses, to prevent or minimize their exposure to injuries. However, ensuring compliance remains difficult, particularly in large or complex sites, which require a time-consuming and usually error-prone manual inspection process. The research proposes an automated PPE detection system utilizing the deep learning model YOLO11, which is trained on the CHVG dataset, to identify in real-time whether workers are adequately equipped with the necessary gear. The proposed PPE-EYE method, using YOLO11x, achieved a mAP50 of 96.9% and an inference time of 7.3 ms, which is sufficient for real-time PPE detection systems, in contrast to previous approaches involving the same dataset, which required 170 ms. The model achieved these results by employing data augmentation and fine-tuning. The proposed solution provides continuous monitoring with reduced human oversight and ensures timely alerts if non-compliance is detected, allowing the site manager to act promptly. It further enhances the effectiveness and reliability of safety inspections, overall site safety, and reduces accidents, ensuring consistency in follow-through of safety procedures to create a safer and more productive working environment for all involved in construction activities. Full article

Despite the agricultural sector’s consistently high injury rates, formal reporting is often limited, leading to sparse national datasets that hinder effective safety interventions. To address this, our study introduces a comprehensive framework leveraging advanced ensemble machine learning (ML) models to predict and interpret the severity of agricultural injuries. We use a unique, manually curated dataset of over 2400 agricultural incidents from AgInjuryNews, a public repository of news reports detailing incidents across the United States. We evaluated six ensemble models, including Gradient Boosting (GB), eXtreme Grading Boosting (XGB), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Histogram-based Gradient Boosting Regression Trees (HistGBRT), and Random Forest (RF), for their accuracy in classifying injury outcomes as fatal or non-fatal. A key contribution of our work is the novel integration of explainable artificial intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), to overcome the “black-box” nature of complex ensemble models. The models demonstrated strong predictive performance, with most achieving an accuracy of approximately 0.71 and an F1-score of 0.81. Through global SHAP analysis, we identified key factors influencing injury severity across the dataset, such as the presence of helmet use, victim age, and the type of injury agent. Additionally, our application of local SHAP analysis revealed how specific variables like location and the victim’s role can have varying impacts depending on the context of the incident. These findings provide actionable, context-aware insights for developing targeted policy and safety interventions for a range of stakeholders, from first responders to policymakers, offering a powerful tool for a more proactive approach to agricultural safety. Full article

Maintaining a safe working environment for construction workers is critical to the improvement of urban areas. Several issues plague the present safety helmet detection technologies utilized on construction sites. Some of these issues include low accuracy, expensive deployment of edge devices, and complex backgrounds. To overcome these obstacles, this paper introduces a detection method that is both efficient and based on an improved version of YOLOv8n. Three components make up the superior algorithm: the C2f-SCConv architecture, the Partial Convolutional Detector (PCD), and Coordinate Attention (CA). Detection, redundancy reduction, and feature localization accuracy are all improved with coordinate attention. To further enhance feature quality, decrease computing cost, and make corrections more effective, a Partial Convolution detector is subsequently constructed. Feature refinement and feature representation are made more effective by using C2f-SCConv instead of the bottleneck C2f module. In comparison to its predecessor, the upgraded YOLOv8n is superior in every respect. It reduced model size by 2.21 MB, increased frame rate by 12.6 percent, decreased FLOPs by 49.9 percent, and had an average accuracy of 94.4 percent. This method is more efficient, quicker, and cheaper to set up on-site than conventional helmet-detection algorithms. Full article