Traffic Sign Recognition Research Articles

НЕЙРОСЕТЕВАЯ СИСТЕМА РАСПОЗНАВАНИЯ ДОРОЖНЫХ ЗНАКОВ

This paper describes the stages and results of developing a neural network system for recognizing road signs. Current problems of image analysis and recognition find their effective solution in neural network methods, while artificial neural networks of convolutional architecture are leading in this subject area. One of the current and practically significant tasks in this area is the recognition of traffic signs. To solve it, it is proposed to use a convolutional neural network and a neural network system implemented on its basis. Describes the use of convolutional neural networks for image classification. To develop a neural network model, the initial stage of preparing a data set - a database of road signs - is described. The publicly available GTSRB set of traffic signs was used to train the neural network. The set contains 43 types of signs, the total number of images is about 39,000. To build a neural network model, 30,000 images of road signs were randomly selected from the original data. To validate the model - 9000 images. The Python programming language was used to develop a convolutional neural network system. In addition, the following libraries were used: OpenCV, NumPy, Scikit-learn, Scikit-image, Matplotlib, Imutils and TensorFlow. Anaconda was used as the development environment. The developed program includes three main files. The modelnet.py file describes the architecture of the neural network. The train.py file contains data processing and training of the neural network to classify traffic signs. The predict.py file presents an implementation of a ready-made convolutional neural network model for classifying road signs. The Adam optimization algorithm was used to directly train the neural network model. The neural network was trained using the fit method from the Keras library. As a result of training, the neural network achieved an accuracy of 94.7% on the training data set and an accuracy of 94.7% on the validation set. Through training, the neural network has learned to recognize and classify different signs effectively, which allows it to be used for automatic sign recognition in real images.

Development of Traffic Light and Road Sign Detection and Recognition Using Deep Learning

Development of Traffic Light and Road Sign Detection and Recognition Using Deep Learning

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

Traffic Road Sign Recognition Using Multi-CNN Model for Autonomous Ground Vehicles

Traffic Road Sign Recognition Using Multi-CNN Model for Autonomous Ground Vehicles

A More Balanced Loss-Reweighting Method for Long-Tailed Traffic Sign Detection and Recognition

A More Balanced Loss-Reweighting Method for Long-Tailed Traffic Sign Detection and Recognition

Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection

Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers. Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques.

A Road Traffic Sign Recognition Method Based on Improved YOLOv5

A Road Traffic Sign Recognition Method Based on Improved YOLOv5

A Comprehensive Survey and Analysis of Traffic Sign Recognition Systems With Hardware Implementation

A Comprehensive Survey and Analysis of Traffic Sign Recognition Systems With Hardware Implementation

Modelling and simulation of an autonomous vehicle based on Alexnet for traffic sign recognition

Modelling and simulation of an autonomous vehicle based on Alexnet for traffic sign recognition

Neural Network-based Multi-Class Traffic-Sign Classification with the German Traffic Sign Recognition Benchmark

Neural Network-based Multi-Class Traffic-Sign Classification with the German Traffic Sign Recognition Benchmark

Efficient traffic sign recognition with YOLOv5

Efficient traffic sign recognition with YOLOv5

Optimisation-enabled deep learning model for traffic sign recognition

Optimisation-enabled deep learning model for traffic sign recognition

Research on Hardware Acceleration of Traffic Sign Recognition Based on Spiking Neural Network and FPGA Platform

Research on Hardware Acceleration of Traffic Sign Recognition Based on Spiking Neural Network and FPGA Platform

Development of Deep Learning Models for Traffic Sign Recognition in Autonomous Vehicles

Development of Deep Learning Models for Traffic Sign Recognition in Autonomous Vehicles

Improved traffic sign recognition model for YOLOv7

Improved traffic sign recognition model for YOLOv7

Traffic Sign Classification using Deep Learning Comparative Study

In recent years, the advancement of deep learning technologies has significantly impacted various domains and the field of transportation is no exception. As the demand for robust and accurate traffic sign classification systems continues to rise, this study presents an in-depth exploration and comparison of various techniques employed in the field. Focusing on state-of-the-art methodologies, we assess the effectiveness and performance of multiple classification approaches for traffic sign recognition. The review presents an extensive survey of the literature, encompassing traditional computer vision methods, machine learning algorithms, and the latest advancements in deep learning. The comparative analysis aims to identify the strengths and limitations of each technique, considering factors such as computational efficiency and accuracy. Additionally, the paper implements four models—CNN, ResNet50, VGG19, and EfficientNetB7—for traffic sign classification on the GTSRB dataset, the accuracy results are reported as 99.25%, 99.28%, 98.95%, and 98.24% respectively.

An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition

In human and other organisms’ perception, olfaction plays a vital role, and biomimetic olfaction models offer a pathway for studying olfaction. The most optimal existing biomimetic olfaction model is the KIII model proposed by Professor Freeman; however, it still exhibits certain limitations. This study aims to address these limitations: In the feature extraction stage, it introduces adaptive histogram equalization, Gaussian filtering, and discrete cosine transform methods, effectively enhancing and extracting high-quality image features, thereby bolstering the model’s recognition capabilities. To tackle the computational cost issue associated with solving the numerical solutions of neuronal dynamics equations in the KIII model, it replaces the original method with the faster Euler method, reducing time expenses while maintaining good recognition results. In the decision-making stage, several different dissimilarity metrics are compared, and the results indicate that the Spearman correlation coefficient performs best in this context. The improved KIII model is applied to a new domain of traffic sign recognition, demonstrating that it outperforms the baseline KIII model and exhibits certain advantages compared to other models.

ETSR-YOLO: An improved multi-scale traffic sign detection algorithm based on YOLOv5.

In the application of driverless technology, current traffic sign recognition methods are susceptible to the influence of ambient light interference, target size changes and complex backgrounds, resulting in reduced recognition accuracy. To address these challenges, this study introduces an optimisation algorithm called ETSR-YOLO, which is based on the YOLOv5s algorithm. First, this study improves the path aggregation network (PANet) of YOLOv5s to enhance multi-scale feature fusion by generating an additional high-resolution feature layer to improve the recognition of YOLOv5s for small-sized objects. Second, the study introduces two improved C3 modules that aim to suppress background noise interference and enhance the feature extraction capabilities of the network. Finally, the study uses the Wise-IoU (WIoU) function in the post-processing stage to improve the learning ability and robustness of the algorithm to different samples. The experimental results show that ETSR-YOLO improves mAP@0.5 by 6.6% on the Tsinghua-Tencent 100K (TT100K) dataset and by 1.9% on the CSUST Chinese Traffic Sign Detection Benchmark 2021 (CCTSDB2021) dataset. In the experiments conducted on the embedded computing platform, ETSR-YOLO demonstrates a short average inference time, thereby affirming its capability to deliver dependable traffic sign detection for intelligent vehicles operating in real-world traffic scenes. The source code and test results of the models used in this study are available at https://github.com/cbrook16/ETSR-YOLO.

DATASET AND IMPROVED YOLOV7 FOR TEXT-BASED TRAFFIC SIGN DETECTION

Abstract. Traffic sign detection is an important part of autonomous driving technology, and it is also important to have a large-scale dataset applicable to Chinese traffic scenarios. The article proposes a text-based self-labelled traffic sign dataset which consists of 3153 images, of which 2903 images are used for training and 250 images are used for validation. And an improved YOLOv7 algorithm is provided that incorporates the BiFormer attention mechanism into the YOLOv7 network to enhance its ability to detect small objects. This approach has the advantage of improved accuracy but may increase runtime. To mitigate this problem, the improved YOLOv7 network undergoes model pruning to compress the model size and increase its speed. Experimental results show that the improved YOLOv7 network in this paper improves the average accuracy by 2.9% while maintaining almost the same speed as the original network. After testing, the model has a real-time effect and practical significance. In conclusion, the text-based self-annotated dataset and the improved YOLOv7 network proposed in this paper have important reference values for text-based traffic sign recognition in automatic driving assistance systems.

Deep Learning-based Traffic Sign Recognition for Autonomous Driverless Vehicles

Deep Learning-based Traffic Sign Recognition for Autonomous Driverless Vehicles