Aug 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 269-276· 0 citations
TL;DR
A CNN-based framework designed to accurately detect and classify traffic signs from input images and can be effectively integrated into intelligent transportation systems, driver assistance technologies, and autonomous vehicle applications is presented.
Abstract
Traffic sign detection and recognition have become essential components of intelligent transportation systems and
Advanced Driver Assistance Systems (ADAS) due to the increasing need for road safety and automated driving. Conventional
traffic sign recognition approaches based on handcrafted features and traditional image processing techniques often struggle to
achieve high accuracy under varying environmental conditions such as poor lighting, occlusions, motion blur, and complex
backgrounds. To overcome these limitations, this work presents a Traffic Sign Detection and Recognition System Using
Convolutional Neural Networks (CNN), designed to accurately detect and classify traffic signs from input images. The proposed
framework utilizes computer vision techniques for image preprocessing, including resizing, normalization, and image
enhancement, followed by deep learning-based feature extraction and classification using a Convolutional Neural Network
(CNN). The CNN automatically learns discriminative visual features such as shapes, colors, and patterns from traffic sign
images, eliminating the need for manual feature engineering. The system is trained and evaluated using the German Traffic
Sign Recognition Benchmark (GTSRB) dataset, which contains more than 50,000 labeled images belonging to 43 different
traffic sign classes. A user-friendly interface is developed using Streamlit, enabling users to upload traffic sign images or capture
images through a webcam for real-time prediction. The trained model classifies the detected traffic sign and displays the
predicted class along with the confidence score. Experimental results are evaluated using Accuracy, Precision, Recall, F1-Score,
Confusion Matrix, and Training Performance Metrics, demonstrating the effectiveness of the proposed CNN-based framework
for accurate and reliable traffic sign recognition. The developed system contributes to improving road safety and can be
effectively integrated into intelligent transportation systems, driver assistance technologies, and autonomous vehicle
applications.
To solve the problem of misclassification of traffic signs under various scale conditions, similar categories with insufficient illumination, image blurriness, partial occlusions, etc., a light-weight multi-scale attention convolutional neural network is introduced. Use a low-resolution image for edge extraction combined with color texture; add residual block layers at last step to increase depth of semantic information reconstruction. Multi-scale convolutional Branches are used to enhance the model's capability of recognizing distant Small Targets and scales changing sample. Introduce a Channel-pace Attention Module to enhance target detection and Tracking Performance in Key Regions. Focal Loss, label smoothing algorithms to improve the model at test time. The top-1 accuracy of experiment results from the German Traffic Sign Recognition Benchmark (GTSRB) was as high as 99.08%, and its F1 scores were around 98.97%. It has strong robustness under conditions such as poor lighting, raindrops, haze, occlusion, and faraway small targets, with moderate model size, computing power consumption, and inference speed. It is appropriate to apply this design in practice.
Zhihao Zou· International Conference on...· 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
In this paper, a comparative study of convolutional neural network (CNN) architectural choices for traffic sign recognition is presented. The effects of different network depths, convolutional kernel sizes, pooling strategies, and activation functions on traffic sign classification performance are examined. In practical scenes, traffic sign images are often affected by illumination changes, scale variation, viewpoint variation, partial occlusion, motion blur, and complex backgrounds. These conditions make accurate traffic sign classification difficult. Therefore, CNNs need to extract discriminative visual features. Experiments are conducted on the German Traffic Sign Recognition Benchmark (GTSRB). The number of learnable parameters is used to analyse model complexity, and a row-normalised confusion matrix is used to examine class-level classification behaviour. Results show that the CNN with three convolutional layers, 4 × 4 convolutional kernels, max pooling, and ReLU activation achieves the best classification performance among the tested configurations. Among the examined architectural choices, network depth shows the largest difference in classification performance, followed by pooling strategy, convolutional kernel size, and activation function.
Bei Zhang, Zhi-Hong Man· International Conference on...· 0 citations
Traffic sign classification is a vital part of intelligent driving, and convolutional classification methods have been widely applied. However, traffic sign images captured in motion are prone to distortion and noise interference. Coupled with the scarcity of labeled images, these factors collectively impair model classification accuracy. To address the above problems, this paper proposes a dynamic threshold semi-supervised image classification method (DTS-RCADC) based on FixMatch, which combines residual convolutional autoencoder and deformable convolution. Firstly, labeled images are used to train the supervised denoising residual convolutional autoencoder for feature extraction and label prediction of noisy images. Afterwards, the attention mechanism selects significant features and locates key regions in original images. Deformable convolution is applied on these regions to effectively guide offset learning. Fusing high-importance features with those extracted by deformable convolution serves as auxiliary information to compensate for insufficient global information extraction, ultimately enhancing image classification performance. Additionally, to overcome labeled data scarcity, we propose a dynamic threshold screening strategy and a weighted label fusion method based on FixMatch, which improve the accuracy and utilization of pseudo-labels. Experimental results on the GTSRB and BelgiumTS datasets demonstrate a significant improvement in classification accuracy.
Unknown authors· Engineering Research Express· 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
Traffic sign detection represents a critical visual perception task in intelligent transportation systems and autonomous driving technologies, where accurate detection directly impacts driving safety. However, existing methods still face two major challenges in practical deployment: difficulty in small object detection and insufficient adaptability to complex environments. To address these issues, this paper proposes a traffic sign detection method integrating frequency domain enhancement and local channel attention based on RT-DETR. First, the backbone network structure is optimized by incorporating Cross Stage Partial (CSP) connections, achieving local-global-frequency domain three-dimensional collaborative feature extraction while maintaining comparable parameter count and computational overhead. Second, we design the EVCGLU (Enhanced Vision Convolutional Gated Linear Unit) module, which sequentially stacks five core components along the residual connection pathway: 3×3 Depthwise Separable Convolution (DWConv), Hidden State Mixer-based State Space Duality (HSM-SSD), Layer Normalization (LayerNorm), 3×3 Depthwise Separable Convolution, and Convolutional Gated Linear Unit (CGLU). This architecture implements local feature-based channel attention, effectively enhancing model robustness. Experimental results on the TT100K dataset demonstrate that the proposed method achieves mAP@0.5 of 83.1% and mAP@0.5-0.95 of 64.7%, representing improvements of 0.2 and 0.7 percentage points over the baseline RT-DETR-R18, respectively. The parameter count is merely 14.54M with computational cost of 48.1G FLOPs, reducing by 27.0% and 15.8% compared to RT-DETR-R18. The inference speed reaches 96 FPS, satisfying the real-time requirements of onboard embedded devices.
Fang Niu, Jia-Jing Sun, Shuang-Qiang Zhang et al.· 2026 8th International Confe...· 0 citations
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