The primary objective of this study is to assess the effect of systematic dataset augmentation on the accuracy of real-time, vision-based Personal Protective Equipment (PPE) detection systems in occupational environments. The PPEDS-1000 dataset was employed, comprising 1,000 expertly annotated images across four PPE usage categories: worker (W), worker with helmet (WH), worker with vest (WV), and worker with both helmet and vest (WHV). An augmented dataset (PPEDS-2600) was derived via controlled geometric transformations (horizontal and vertical flips), additive Gaussian noise, and Gaussian blur. Each dataset is partitioned using an 80/10/10 train–validation–test split and utilized to train five YOLOv8 model variants (nano through extra-large). The evaluation metrics include precision, recall, F1-score, mean average precision at an IoU threshold of 0.5 (mAP50), and mean average precision averaged over IoU thresholds from 0.5 to 0.95 (mAP50-95). The experimental results demonstrate that augmentation elevates mAP50 from 77.7% on PPEDS-1000 to 94.8% on PPEDS-2600, thereby substantiating the hypothesis that targeted augmentation markedly enhances detection performance. The findings indicate that the present work establishes a rigorous benchmark for real-time PPE compliance monitoring.
Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning model is developed. You Only Look Once – Neural Architecture Search (YOLO-NAS) algorithm was chosen to be used in the model because of its fast and high-quality detection process. A dataset with images containing healthy workers wearing helmets & safety vests (with some workers appearing in different safety vests) is collected and used as the training set. The resulting model is then saved for future usage in making predictions with new images. Then, this model is connected to Streamlit, which provides a convenient Web-based user interface through which a user can provide an image as an input to the model. The trained neural network is applied to the input image, and all detected objects that correspond to helmet & safety vest are put in bounding boxes on the input image. Moreover, each of the bounding boxes is labeled with the name of the object and confidence score of the detection. Thus, a clear visualization of the safety equipment worn by the worker is provided. Overall, the developed detection system significantly decreases the human intervention needed for visual verification of the safety helmet or vest. The described system is a user-friendly and cost-effective way of evaluating workplace safety compliance.
S. Vijayakumar, Loganathan Nachimuthu, Balasubramaniam C et al.· 2026 International Conferenc...· 0 citations
A chair designed for office monitoring capable of accurately identifying ten representative seated postures and measuring environmental factors was developed and validated and presented an initial promising first step for an ergonomic office management solution.
Tatiana Teixeira, Guilherme Barbosa, B. Areias et al.· Italian National Conference...· 0 citations
Personal protective equipment (PPE) detection is a critical task in construction safety management, yet real-world construction scenes are often affected by occlusion, scale variation, complex backgrounds, and small-object interference. To evaluate the applicability of lightweight object detection for practical site monitoring, this study investigates a six-class PPE detection task in construction scenes based on YOLO11n, covering person, hard hat, vest, safety boots, gloves, and mask. Experiments were conducted on a public PPE dataset containing 1,617 images, which were divided into 1,100 training images, 304 validation images, and 213 test images. The model was trained with an input size of 640 × 640 for 50 epochs and evaluated using Precision, Recall, mAP@0.5, mAP@0.5:0.95, and inference speed. The results show that YOLO11n achieved Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.821, 0.872, 0.846, and 0.566, respectively, on the test set, while maintaining an inference speed of approximately 103 FPS on an RTX 4050 Laptop GPU. Class-wise analysis indicates that person, hard hat, and vest were detected more reliably, whereas mask, gloves, and safety boots remained more challenging because of their small scale, occlusion sensitivity, and ambiguous visual features. Overall, the results demonstrate that lightweight YOLO11n provides a reasonable balance between detection accuracy and real-time performance for six-class PPE monitoring in construction environments, and it can serve as an effective baseline for future improvement in small-object enhancement and deployment-oriented optimization.
Yin Jian, D. Ying, Ying Sim et al.· Journal of Intelligent Decis...· 0 citations
The proposed Media Pipe–CNN framework provides an efficient, accurate, and marker less solution for automated ergonomic risk assessment, supporting intelligent occupational safety management, continuous workplace monitoring, and the implementation of smart manufacturing systems aligned with Industry 4.0 initiatives.
Rahmadwati Rahmadwati, Farrel Rafif Ferdian, Y. Sumantri et al.· International Journal of Eng...· 0 citations
This work proposes a detection framework called distillation alignment YOLO (DA-YOLO) for PPE detection and introduces a teacher–student distillation framework with consistency constraints across predictions and high-order features extracted from baseline that enables the student model to achieve strong generalization while maintaining low computational cost.
Chonghua Zhou, Ruixuan Zhang, Yixi Fu et al.· Multimedia Systems· 0 citations
This paper proposes a data-driven FFH detection method that integrates multiple complementary features into a unified score-based model, achieving a practical balance between detection sensitivity, false-positive suppression, computational efficiency, and real-time feasibility.
SangHyeok Kim, Daejin Park, Soon Ju Kang· Big Data and Cognitive Compu...· 0 citations
