Aug 2026· International Journal of Data Science and IoT Management System· 0 citations· 9 references
TL;DR
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.
Abstract
This research uses the most recent YOLOv10 object detection architecture to demonstrate a sophisticated computer vision system for real-time safety helmet detection and license plate recognition. The main goal is to improve vehicle monitoring and workplace safety by automatically recognising people who are not wearing safety helmets in industrial zones and recording license plates for regulatory and surveillance purposes. Efficient multi-object identification in difficult situations is made possible by YOLOv10, which is renowned for its exceptional speed and accuracy. To ensure reliable performance, the system is trained using annotated datasets that include a variety of helmet types and car plates under various circumstances. Construction workers' risk of suffering head injuries in highaltitude falls can be significantly decreased by donning safety helmets. 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.
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
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
Ship engine rooms present difficult visual conditions, including low illumination, oil mist or steam, equipment occlusion, overhead viewpoints and densely distributed hazardous zones. To link safety-helmet recognition with hazard-zone warning under these conditions, this study proposes a YOLOv13s model-selection and electronic-fence fusion method. YOLOv5s, YOLOv8s, YOLOv11s, YOLOv13s and YOLOv13-EEAttne were compared on the same ship-engine-room helmet dataset under unified training settings. Scene subsets were further constructed from image brightness, target-box pixel area, inter-box overlap and target count, covering normal and low illumination, near-, mid- and far-range targets, and no, partial and severe occlusion. YOLOv13s was re-evaluated on these subsets, and its detection outputs were connected to foot-point electronic-fence rules and multi-frame confirmation. YOLOv13s achieved a final Precision of 0.9559, mAP@0.5 of 0.9201 and mAP@0.5:0.95 of 0.6795, outperforming the other compared models overall. The model contained approximately 9.55 million parameters, required 21.62 GFLOPs at an input size of 640 and reached 43.25 FPS on an RTX 4060 Laptop GPU. Scene-subset validation showed mAP@0.5:0.95 values of 0.672 and 0.597 under normal and low illumination, and 0.714, 0.673 and 0.470 for near-, mid- and far-range targets. These results indicate that distant small targets remain the main challenge and that YOLOv13s, combined with electronic-fence rules, provides a feasible detection basis for safety-helmet warning and hazardous-zone monitoring in ship engine rooms.
Jingpeng Li, Chao Wang, Fengming Zhao et al.· Journal of Computer Science...· 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
Safety helmet wearing detection is a crucial component of safety management in construction sites. Traditional detection methods based on manual monitoring are inefficient, while existing deep learning models often suffer from high computational costs and poor detection performance for small or occluded targets. To address these issues, this paper proposes a lightweight detection algorithm named GCW-YOLOv8. Firstly, the Ghost Module is introduced into the backbone network to replace the conventional convolution layers, significantly reducing the number of parameters and floating-point operations (FLOPs) while maintaining feature extraction capability. Secondly, the Coordinate Attention (CA) mechanism is embedded into the neck network to enhance the model's sensitivity to spatial location and channel information, thereby improving the detection accuracy of small targets. Finally, the Wise-IoU (WIoU) loss function is adopted to replace the original CIoU loss, utilizing a dynamic non-monotonic focusing mechanism to optimize the gradient assignment for low-quality samples. Experimental results on the safety helmet dataset show that the proposed algorithm achieves a mean Average Precision (mAP@0.5) of 94.5%, while the inference speed reaches 108 FPS. Compared with the baseline YOLOv8n, our method improves detection accuracy by 1.4% while reducing parameter count by 34.4%, achieving a superior balance between accuracy and efficiency for smart construction site applications.
Zheng Re, Zhisen Ren, Qianru Liu et al.· International Conference on...· 0 citations
Real-time firearms detection is key for ensuring safety in public spaces and security-sensitive environments. In this paper, we present a computer vision approach using the YOLOv8 object detection model to identify whether an individual is carrying a firearm. The model’s architecture allows for rapid and accurate detection, making it suitable for real-time applications. A custom dataset of images was used for training and validation, with preprocessing techniques applied to enhance model generalization. The proposed system was evaluated on key performance metrics, achieving high precision and recall in detecting both exposed and partially concealed firearms. The ability to operate in real-time offers a promising solution for improving security measures in various contexts. Future work will focus on refining the system to reduce false positives and expanding the dataset to include a broader range of scenarios.
R. Mustafovski, T. Shuminoski, Membe et al.· Infocommunications journal· 0 citations
