Search Results (13)

To mitigate the difficulty of real-time PPE compliance monitoring in electronic factories, we developed an intelligent platform combining You Only Look Once version 10 (YOLOv10) real-time detection with Large Language Model Meta AI (Llama) 3.2 Vision multimodal large language model. Using real and simulated factory footage annotated via Roboflow, YOLOv10 detects violations, and Llama 3.2 vision generates natural-language descriptions. Results showed a 94.8% accuracy, a 94.81% recall, and a 91.82% F1-score, with robust performance under complex lighting/occlusion and clear semantic reports. The platform significantly improves factory safety and management efficiency, offering strong practical value. Full article

Automated face mask detection remains an important component of hygiene compliance, occupational safety, and public health monitoring, even in post-pandemic environments where real-time and non-intrusive surveillance is required. Traditional deep learning models provide strong recognition performance but are often impractical for deployment on embedded and edge devices due to their computational and energy demands. Recent research has therefore emphasized lightweight and hybrid architectures that seek to preserve detection accuracy while reducing model complexity, inference latency, and power consumption. This review presents an architecture-centered synthesis of face mask detection systems, examining conventional convolutional models, lightweight convolutional networks such as the MobileNet family, and hybrid frameworks that integrate efficient backbones with optimized detection heads. Comparative analysis of reported results highlights key trade-offs between accuracy, efficiency, and deployment feasibility under heterogeneous datasets, evaluation protocols, and hardware settings. Open challenges, including improper mask detection, domain adaptation, model compression, and the extension of mask detection toward broader Personal Protective Equipment (PPE) compliance monitoring, are discussed to outline a forward-looking research agenda. Overall, this review consolidates current understanding of architectural design strategies for face mask detection and provides guidance for developing scalable, robust, and real-time deep learning solutions suitable for embedded and mobile platforms. Full article

Safety monitoring of scaffolding operations is essential for preventing accidents in high-altitude construction. This study proposes an integrated computer vision and multi-criterion decision-making (MCDM) framework that combines object detection, pose estimation, Analytic Network Process (ANP) and ELECTRE III methods to evaluate safety risks of construction workers. Specifically, computer vision techniques are employed to extract objective visual evidence related to workers’ behaviors, protective equipment (PPE) usage, and working environments, which serve as the basis for subsequent safety risk quantification. A four-criterion system, including action risk, PPE compliance, working height, and structural integrity, is established. Weights are determined via the ANP, and risk ranking is conducted using ELECTRE III. Experiments on a self-built dataset achieved an mAP@0.5 of 92.3%, a segmentation IoU of 67.2%, and a pose OKS@0.5 of 89.6%. The evaluation results correlate strongly with expert assessments (Kendall’s τ = 0.79). The proposed framework effectively identifies unsafe behaviors and quantifies safety risks, providing reliable decision support for intelligent construction safety management. 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

Ensuring hygiene compliance in regulated environments—such as food processing facilities, hospitals, and public indoor spaces—requires reliable detection of personal protective equipment (PPE) usage, including gloves, face masks, and hairnets. Manual inspection is labor-intensive and unsuitable for continuous, real-time enforcement. This study benchmarks three lightweight object detection models—YOLOv8n, YOLOv10n, and YOLOv12n—for automated PPE compliance monitoring using a large curated dataset of over 31,000 annotated images. The dataset spans seven classes representing both compliant and non-compliant conditions: glove, no_glove, mask, no_mask, incorrect_mask, hairnet, and no_hairnet. All evaluations were conducted using both detection accuracy metrics (mAP@50, mAP@50–95, precision, recall) and deployment-relevant efficiency metrics (inference speed, model size, GFLOPs). Among the three models, YOLOv10n achieved the highest mAP@50 (85.7%) while maintaining competitive efficiency, indicating strong suitability for resource-constrained IoT-integrated deployments. YOLOv8n provided the highest localization accuracy at stricter thresholds (mAP@50–95), while YOLOv12n favored ultra-lightweight operation at the cost of reduced accuracy. The results provide practical guidance for selecting nano-scale detection models in real-time hygiene compliance systems and contribute a reproducible, deployment-aware evaluation framework for computer vision in hygiene-critical settings. Full article

This study introduces ESPCN-YOLO, an innovative deep learning framework designed to enhance the detection accuracy of Personal Protective Equipment (PPE) under challenging conditions, including low-light environments, long-distance scenarios, and small object detection. The proposed system integrates a YOLOv8-based object detection model with an Efficient Sub-Pixel Convolutional Neural Network (ESPCN) to perform real-time super-resolution enhancement on low-resolution footage. The framework was trained on a custom dataset containing 21,750 annotated images categorized into four PPE classes: helmets, shoes, vests, and persons. Extensive experiments were conducted under varying conditions, including distances ranging from 4 to 14 m, resolutions of 640 × 480 and 1920 × 1080, and brightness levels adjusted from −90% to +70%. The results demonstrate that integrating an ESPCN (3×) with YOLOv8 significantly improves detection accuracy, particularly for small objects and poorly illuminated environments. The model achieved a mean average precision (mAP@0.5) of 0.922 and a stringent mAP@0.5:0.95 of 0.741. Additionally, an automated alert system was implemented to enable real-time PPE compliance monitoring. This study highlights the effectiveness of super-resolution enhancement in increasing detection robustness and provides a practical solution for real-time safety monitoring in industrial environments. Full article

Porcine proliferative enteropathy (PPE) is an infectious disease in pigs, caused by Lawsonia intracellularis (LI), affecting their intestines during growth and finishing stages, leading to higher production costs. Current detection methods for LI face two main challenges, delayed results and high costs, making them impractical for large-scale pig farming epidemiological surveys. This study developed a TaqMan-qPCR method using specific probes and primers based on the LI aspartate ammonia lyase genes from GenBank, completing detection in just 45 min. After optimizing reaction conditions, sensitivity analysis revealed that the detection limit of this method was 4.6 copies/μL targeting standard plasmids. The results of the specificity analysis showed no cross-reactivity with other common porcine pathogens, highlighting its specificity. The inter- and intra-group coefficients of variation were both <1%, indicating high reproducibility. Furthermore, the TaqMan-qPCR demonstrated 100% relative sensitivity, and a 92.50% compliance rate compared to conventional PCR, suggesting it could be a complement to the conventional PCR method. In summary, the TaqMan-qPCR method established in this study is not only suitable for epidemiological investigations and early qualitative and quantitative diagnosis of proliferative enteropathy in pigs, but it is also valuable for studying the biological characteristics of LI. Full article

Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system’s performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments. Full article

Occupational sectors are perennially challenged by the potential for workplace accidents, particularly in roles involving tools and machinery. A notable cause of such accidents is the inadequate use of Personal Protective Equipment (PPE), essential in preventing injuries and illnesses. This risk is not confined to workplaces alone but extends to educational settings with practical activities, like manufacturing teaching laboratories in universities. Current methods for monitoring and ensuring proper PPE usage especially in the laboratories are limited, lacking in real-time and accurate detection capabilities. This study addresses this gap by developing a visual-based, deep learning system specifically tailored for assessing PPE usage in manufacturing teaching laboratories. The method of choice for object detection in this study is You Only Look Once (YOLO) algorithms, encompassing YOLOv4, YOLOv5, and YOLOv6. YOLO processes images in a single pass through its architecture, in which its efficiency allows for real-time detection. The novel contribution of this study lies in its computer vision models, adept at not only detecting compliance but also assessing adequacy of PPE usage. The result indicates that the proposed computer vision models achieve high accuracy for detection of PPE usage compliance and adequacy with a mAP value of 0.757 and an F1-score of 0.744, obtained with the YOLOv5 model. The implementation of a deep learning system for PPE compliance in manufacturing teaching laboratories could markedly improve safety, preventing accidents and injuries through real-time compliance monitoring. Its effectiveness and adaptability could set a precedent for safety protocols in various educational settings, fostering a wider culture of safety and compliance. Full article

Accidents occur in the construction industry as a result of non-compliance with personal protective equipment (PPE). As a result of diverse environments, it is difficult to detect PPE automatically. Traditional image detection models like convolutional neural network (CNN) and vision transformer (ViT) struggle to capture both local and global features in construction safety. This study introduces a new approach for automating the detection of personal protective equipment (PPE) in the construction industry, called PPE_Swin. By combining global and local feature extraction using the self-attention mechanism based on Swin-Unet, we address challenges related to accurate segmentation, robustness to image variations, and generalization across different environments. In order to train and evaluate our system, we have compiled a new dataset, which provides more reliable and accurate detection of personal protective equipment (PPE) in diverse construction scenarios. Our approach achieves a remarkable 97% accuracy in detecting workers with and without PPE, surpassing existing state-of-the-art methods. This research presents an effective solution for enhancing worker safety on construction sites by automating PPE compliance detection. Full article

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time. Full article

Construction hazards occur at any time in outfield test sites and frequently result from improper interactions between objects. The majority of casualties might be avoided by following on-site regulations. However, workers may be unable to comply with the safety regulations fully because of stress, fatigue, or negligence. The development of deep-learning-based computer vision and on-site video surveillance facilitates safety inspections, but automatic hazard identification is often limited due to the semantic gap. This paper proposes an automatic hazard identification method that integrates on-site scene graph generation and domain-specific knowledge extraction. A BERT-based information extraction model is presented to automatically extract the key regulatory information from outfield work safety requirements. Subsequently, an on-site scene parsing model is introduced for detecting interaction between objects in images. An automatic safety checking approach is also established to perform PPE compliance checks by integrating detected textual and visual relational information. Experimental results show that our proposed method achieves strong performance in various metrics on self-built and widely used public datasets. The proposed method can precisely extract relational information from visual and text modalities to facilitate on-site hazard identification. Full article

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students. Full article