Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 701-705· 0 citations· 4 references
Abstract
Traffic signs are road facilities that communicate, direct, limit, caution or teach information, whether in the form of words or symbols. As the demand for the intelligence of vehicles is on the rise, there is a great need to invent and identify traffic signs automatically using technology. Nonetheless, the identification of traffic signs is not that easy, as a number of negative parameters exist, such as bad weather, change of perspective, physical impairment, and others. Currently, most of the available text mining algorithms help in processing the whole data to identify the traffic sign images. In this proposed research, an extensive sign board detection algorithm is developed where AlexNet image classification algorithm forms the premier stage. It is mainly focussed on the process of detection with the improvement of the traffic signs using a boundary enhancement algorithm along with the average filter. This helps in reducing the noise and enhances the sign to be fed into the classifier system. This approach enhances precision of 99.27%, sensitivity of 99.41% and specificity of 99.47%. Thus the proposed algorithm minimizes the time taken to detect the traffic sign in misty weather.
In India, it is a country that has a well-known and dense road network. On these roads, the traffic signals benefit the driver in delightful the driving conclusions appropriately. These traffic signals effort as the noiseless helper to the driver. These indicators tell approximately the complaint of the road forward. So that the driver can take the improved and effective conclusion nearby how to drive and what are the influences to take care of. Normally, these signs are used to presage the driver nearby the possessions which are constrained or which can help the driver to sidestep the coincidence. Occasionally these indicators are correspondingly rummage-sale to border the driver to go overhead the limitation restricted on that road, like the rapidity limit. Every driver on road must be accustomed with all these signs and this is confirmed while philanthropic driving license to any driver.
In this article, describe about the technique for road traffic sign detection and recognition. It is analysis the process of convolutional neural networks (CNNs) and other approaches, underlining their assets, limitations, and applicability to real-time scenarios. the article also discusses about the compare analysis of traffic sign recognition systems.
Keywords— Road Traffic Sign Detection, Traffic Sign Recognition (TSR), Deep Learning, Convolutional Neural Networks (CNN), Object Detection, YOLO, Faster R-CNN, Single Shot Detector (SSD).
Jitendra Sheetlani, Sheetesh Sad· International Journal of Cre...· 0 citations
The Advanced Driver Assistance Systems (ADAS) have a significance in enhancing road safety by identifying the
traffic signs and road surface problems in real-time. This research paper compares three models of object detection that are
based on YOLO algorithm and are YOLOv5, YOLOv7, and YOLOv8 for detecting Indian traffic signs and potholes. The
approach used in this research involved the steps of collecting dataset, annotating the data, processing the data, augmenting the
data, and training the model. The results after conducting the experiments show that every model has its own particular
functionalities. In case of traffic signs detection, YOLOv5 got the highest precision of 99.3 percent, however, YOLOv8 got the
highest recall of 83.3 percent and mAP50 of 88.7 percent, showing the capability of identifying the sign on different road
conditions. In case of pothole detection, YOLOv5 gave the best precision of 83.1 percent, but YOLOv7 performed best overall
with retrieval of recall of 76.7 percent and mAP of 79.0 percent and mAP range from 50 to 95 of 48.3 percent. The comparison
proves that the YOLO models of object detection are of great use in real-time ADAS and show good efficiency in road scene
perception.
G. Gayathri, C. Chandrika· International Journal for Re...· 0 citations
Ensuring road safety requires timely and accurate detection of lanes and roadside traffic signs, which are essential for assisting drivers and reducing accident risks. However, robust simultaneous detection remains challenging due to variations in illumination, occlusions, complex backgrounds, and diverse road conditions. This research introduces a novel vision-based system for lane detection and roadside traffic sign recognition using advanced artificial neural network architectures. In the pre-processing stage, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to enhance image visibility under varying lighting conditions. For classification, a Self-Artificial Attentive Gated Neuro-Recurrent Unit (SAAG-NRU) is proposed, a novel architecture integrating deep artificial neural network (ANN) layers, Gated Recurrent Units (GRU), and a self-attention mechanism, enabling sequential refinement of features and prioritization of the most safety-relevant cues. In addition, YOLOv11 was utilized to train and detect traffic signs exclusively. For lane detection, a lane mask-based approach is implemented to accurately segment and highlight lane boundaries. The trained model was integrated into a Tkinter-based Graphical User Interface (GUI) to visualize real-time detection outputs effectively. Implemented using Python-based tools, the framework is validated against benchmark lane and traffic signs, demonstrating better performance compared to existing systems in terms of lane detection, achieving 91.11% precision, and traffic sign detection, attaining 96.88% precision. Therefore, the proposed system delivers fast, accurate, and robust simultaneous lane and traffic sign detection, significantly improving real-time road safety and driver assistance.
Viraj Sonawane, B. Agarkar, Sachin Chaudhari· International Journal of Adv...· 0 citations
One of the most important features of Advanced Driver Assistance Systems (ADAS) and Intelligent Transportation Systems (ITS), namely Traffic Sign Recognition (TSR), is a computer vision system that can automatically recognize and classify traffic signs to ensure road safety. In the proposed research work, a dataset provided by Kaggle on traffic signs containing 6117 images of various categories of traffic signs such as speed limit, stop, no entry, yield, pedestrian crossing, etc., has been used to propose a framework for classification of traffic signs using Deep Learning (DL) techniques. In the proposed research work, a combination of two architectures, namely VGG16 and DenseNet121, has been used by employing feature level ensemble techniques to improve the efficiency of the developed ensemble architecture by comparing the efficiency of the proposed ensemble architecture with existing architectures. The developed ensemble architecture has been found to attain an accuracy of 99.54%, precision of 99.69%, recall of 99.54%, and F1-score of 99.57%, which shows that the proposed ensemble architecture is productive and reliable compared to existing architectures.
Sai Harshitha Narlapati, Sruthi Bajjuri, Prathap Kumar Ravula et al.· 2026 4th International Confe...· 0 citations
Traffic sign detection is an integral part of Advanced Driver Assistance Systems (ADAS) and self-driving cars wherein correct and timely detection helps ensure a safer driving experience. However, currently available models are trained on benchmark datasets that pertain to European or Chinese traffic scenarios. Such models lack the efficiency required for traffic scenarios in India due to multiple factors such as the variety of sign shapes and languages present, occlusions, varied light intensities and prevalence of smaller objects. In this paper, a region-based traffic sign detection system has been proposed using the enhanced YOLOv8 framework for Indian traffic scenarios. A subset of the Mapillary Traffic Sign Dataset (MTSD) has been curated, annotated and data-augmented with mosaic augmentation, change in brightness, adaptive scaling, and high-resolution training to overcome challenges posed by occlusions, varying light intensity and distant traffic signs.
L. M, S. N., Sohan Js et al.· 2026 7th International Confe...· 0 citations
Automatic Traffic Sign Recognition is a crucial sub-task for autonomous driving, playing a key role in reducing accidents and enabling safer, more intelligent navigation on the roads. This challenge remains largely unresolved in the Arabic world, unlike other regions. This paper reviews the current state-of-the-art in automatic Arabic road-signs recognition; it proposes a new model, which improves the best performance of existing databases; it merges the Arabic Traffic Signs collection with the Jordanian dataset, and it uses several data augmentation techniques to create the largest publicly available database in the Arabic world to date. Finally, it gives a benchmark algorithm for the newly introduced collection. The proposed model is a variation of the lightweight Residual Net architecture, which has been improved by a significant reduction in the number of parameters, minimization of down-sampling to maintain as much spatial detail as possible, and the incorporation of both Dropout and Batch Normalization to ensure better regularization and overfitting prevention. The performance of the introduced model on the Arabic Traffic Sign database surpasses the current state of the art in both accuracy and training time. The same architecture is also very successful when tested on the newly introduced picture collection.
Elena Battini Sonmez, Hamza Arnaout, Albano Younes· International Advanced Resea...· 0 citations
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