2026· EPJ Web of Conferences· Vol 377, pp. 02011· 0 citations· 9 references
TL;DR
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.
Abstract
The application of computer vision technology in automation systems plays a crucial role in improving the efficiency of occupational safety monitoring in industrial environments. This study developed a YOLOv8-based visual detection application in ONNX format to identify safety helmet violations in real-time. The system was developed using Python with a Tkinter-based user interface and integrated with a Flask web dashboard that displays violation log data. The application can accept video input from various sources, including webcams, USB cameras, and IP cameras, to classify the type of helmet being used. Only orange and white safety helmets are considered valid. Detecting a new helmet, a motorcycle helmet, or a helmet with an inappropriate colour will trigger an alarm and store the image as evidence of the violation. The YOLOv8 model was trained on a six-class dataset and demonstrated good performance, with a precision of 0.921, a recall of 0.859, an mAP50 value of 0.919, and an mAP50-95 value of 0.619. System evaluation demonstrated the application's stability and accuracy in computer vision-based automated surveillance.
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
The design of a low-cost driver assistance system (DAS) using monocular camera input and artificial intelligence to enhance road awareness consists of using low-cost sensors instead of costly configurable sensors used in typical systems. The hybrid perception architecture of this system incorporates deep learning (via optimized YOLOv8) and traditional computer vision techniques to achieve high accuracy in detecting vehicles and pedestrians, which is consistent regardless of traffic conditions. Additionally, the hybrid lane detection algorithm combines edge-filtering techniques with geometric models to allow for lane detection in low-light or poorly marked lane conditions. Also, the development of a modular processing pipeline allows for real-time video preprocessing, feature extraction and risk assessment, therefore requiring less computational resources than standard DAS systems. Finally, testing showed that this DAS system consistently performs in real-time and achieves an acceptable degree of accuracy, irrespective of environmental conditions. The DAS system provides a common structure for a variety of vision techniques and can be scaled and constructed for a lower cost than most current DAS solutions, thereby facilitating the development of intelligent transportation systems and increasing access to transportation technology.
Poola Joshika, C. Dharshana, Shreya Sridharan et al.· 2026 6th International Confe...· 0 citations
Assistive technologies based on computer vision have a huge potential in improving mobility of visually impaired people. However, they are not very widely adopted because they depend on high-power processors, cloud access, or complicated sensor configurations. This paper outlines a low-cost and lightweight wearable vision-assist system that is completely edge-based and provides real-time information in the environment. The framework is constructed using the Raspberry Pi Zero 2W and a lightweight object detection model optimized to run on a monocular camera on the device using the Tensorflow Lite. The design proposed offers object identification, rough distance estimation, and spatial position (right, left, centre) with audio feedback in real-time to enhance MSIA both indoors and outdoors. Experimental analysis demonstrates a mean detection rate of 92%, spatial localization rate of 85% and audio feedback latency of below 2 seconds at a power consumption in the range of less than 5W. These findings indicate that implementing effective assistive vision systems on ultra-low-power embedded systems is achievable and can be used in practice as a portable solution for everyday use.
Harika B, T. Yerram, Manasa Katukuri et al.· Turkish Journal of Engineeri...· 0 citations
Safety helmet compliance monitoring remains challenging because helmets often occupy only a few pixels in wide-area surveillance images. This study tackles these small-object difficulties in vision-based safety-helmet compliance monitoring (helmet vs. no-helmet) using wide-area surveillance imagery. An enhanced You Only Look Once version 10 (YOLOv10) detector is proposed by integrating Omni-Dimensional Dynamic Convolution (ODConv) into the backbone, an Efficient Multi-scale Attention-guided Bidirectional Feature Pyramid Network (EMA-BiFPN) for multi-scale feature fusion, a four-head detection scheme, and the Minimum Points Distance Intersection over Union (MPDIoU) regression loss. Across the experiments, the proposed detector reached an mAP50 of 94.28, with AP50 values of 96.55 for the helmet class and 92.01 for the no-helmet class, exceeding the performance of the YOLOv10 baseline and other benchmark detectors. The most notable gains appeared for extremely small and small targets ([Formula: see text] = 86.10, [Formula: see text] = 91.55), reflecting improved localization of helmets at long distances. Overall, these findings indicate that the method is a strong candidate for deployment-focused helmet-compliance monitoring in large-scale construction settings, although performance limitations remain most evident in far-field views and highly crowded scenes.
Seunghyeon Wang, Enlian Zhang, Rong-Lu Hong et al.· Scientific Reports· 1 citation
The paper explores how the classical approaches to image processing have been transformed to deep learning based methods such as their application in object detection and tracking, activity recognition, anomaly detection and facial recognition, and provides the future research direction, which is important to the next-generation intelligent surveillance systems.
Ajay Krishnan· International Journal of Mod...· 0 citations
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