Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 776-781· 0 citations· 21 references
Abstract
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.
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.
Öyküm Akar, Hasan Selim, Orhan Er et al.· Intelligenza Artificiale· 0 citations
This study suggests an enhanced safety helmet detection method based on YOLOv10 to solve the low detection accuracy of current algorithms for small objects and complicated settings in different situations.
Iqra Aziza Khatoon, Dr. Safia Khanam· International Journal of Dat...· 0 citations
An intelligent safety belt model with self-identification and self-sensing capabilities is developed, which significantly improves safety supervision for work at height and provides proactive protection for personnel performing elevated tasks.
Wending Li, Jian-Lun Lin, Minghui Lin et al.· International Conference on...· 0 citations
A YOLOv8-based visual detection application in ONNX format to identify safety helmet violations in real-time to demonstrate the application's stability and accuracy in computer vision-based automated surveillance.
S. Syufrijal, Heri Firmansyah, Christophorus Mrc Yuda et al.· EPJ Web of Conferences· 0 citations
Motorcycle traffic accidents have been on the rise because of low compliance with helmet laws and there is need for automated traffic monitoring and enforcement systems. This paper proposes a traffic enforcement solution that combines YOLOv8 based object detection and PaddleOCR for automatic violation detection and license plate recognition of motorcyclists. The system will detect motorcycles, riders, helmets and license plates from traffic images, identify the helmet violation cases by analyzing their spatial relationship and extract the registration numbers of the vehicles for automatic enforcement. The solution is trained and tested using 4,169 traffic images that have been labeled. The helmet detection model obtains an mAP@50 score of 0.983, and the license plate detection module gives localization of the license plates which makes them recognizable by the OCR. There is a Flask based web app that allows users to upload images, detect violations, generate evidence, and notify fines.
The construction safety of workers in hydraulic construction sites that are crowded and difficult to manage is very serious. When personnel movement is frequent, the status of workers wearing safety helmets is difficult to monitor in real time. The focus of this study is the design of HDS-DETR model which is aimed to improve the safety recognition in hydraulic construction projects. Improvements were achieved by integrating the C2f-HDRAB Module to the RT-DETR model to strengthen the model's ability to detect features, the D-Attention mechanism to improve the model's ability to recognize important features, and SlimNeck architecture was implemented to improve the model's ability to efficiently fuse features. The results of the experiments reflect that the accuracy achieved was 94.1% and 89.6% of the improved model offered by the dedicated dataset in recall, and 94.9% of the mean Average Precision at IoU threshold 0.5, which is a 3.7% increase in the original model. The ablation tests demonstrate the effectiveness of the correction of modules and the proposed design is aimed at the complex nature of hydraulic construction, and provides real-time hard hat wearing monitoring. Safety management of the hydraulic engineering construction project provides support and improves the safety condition recognition in smart water conservancy construction projects.
Shousong Liu, Qiulei Zhang, J. Mi et al.· International Conference on...· 0 citations
