Traffic Sign Recognition Using Deep Learning

Abstract

Traffic sign recognition is a crucial component in advanced driver assistance systems and autonomous vehicles, enhancing road safety and overall transportation efficiency. Deep learning, specifically convolutional neural networks (CNNs), has emerged as a powerful tool for image based recognition tasks. The proposed deep learning-based traffic sign recognition system exhibits promising results, providing a foundation for the development of intelligent transportation systems. The trained model demonstrates remarkable accuracy in recognizing a wide range of traffic signs under different scenarios, including challenging lighting conditions and occlusions. The system\'s real-time performance is evaluated on both simulated and actual road scenarios, showcasing its feasibility for deployment in practical driving environments. The integration of such technology into vehicles holds great potential for enhancing road safety, reducing accidents, and contributing to the evolution of autonomous driving technology.

Introduction

I. INTRODUCTION

Traffic sign recognition has deep learning techniques for traffic sign recognition, offering valuable insights for researchers, engineers, and practitioners working in the field of intelligent transportation systems and autonomous driving. The presented results demonstrate the feasibility and effectiveness of the proposed approach, paving the way for further advancements in this critical area of research. This research contributes valuable insights into the design and optimization of deep learning models for traffic sign recognition, paving the way for further advancements in intelligent transportation systems..

II. LITERATURE SURVEY

1. Traffic sign Recognition Based on Multi-feature Fusion and ELM Classifier

This study presents a new and effective approach to recognizing traffic signs by combining different sets of features that are both complementary and discriminative. The feature sets used are the histogram of oriented gradients (HOG) feature, Gabor feature, and Compound local binary pattern (CLBP) feature. The extreme learning machine (ELM) algorithm is employed for classification. The performance of the proposed method is evaluated on two benchmark datasets: the German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification (BTSC) Datasets. The experimental results indicate that each individual feature achieves high accuracy, and the combination of all three features results in good complementarity and fast recognition rates, making it suitable for real-time applications.

2. Data Debiased traffic sign Recognition using MSERs and CNN

This research paper presents a novel traffic sign recognition algorithm that is not influenced by dataset bias. Traffic sign recognition heavily relies on colour information, which can be affected by various factors such as weather conditions, illumination, and camera variations. To overcome these challenges, the proposed approach involves both traffic sign detection and classification. Specifically, a detection module is employed that enhances red and blue colour with MSERs to improve the extraction of candidate regions of traffic signs. A Bayesian classifier with a DtB feature is then used to detect the traffic signs. The detected traffic signs are subsequently classified using spatial transformer networks based on convolutional neural networks. The proposed approach is evaluated on public datasets, and the results indicate that it achieves competitive accuracy without requiring a training dataset.

3. Traffic Signs Recognition and Classification Based on Deep Feature Learning

This paper proposes a new method for recognizing and classifying traffic signs using deep feature learning, which is crucial for unmanned automatic driving. While several approaches have been proposed in the past, their performance is still not adequate for real-world applications. the proposed method employs a combination of Convolutional Neural Network and Support Vector Machine (CNN-SVM) for traffic sign recognition and classification. The YCbCr colour space is utilized in the CNN to extract features from colour channels. Subsequently, an SVM classifier is used to classify the extracted features. The effectiveness of this approach is evaluated on a real-world dataset consisting of images and videos captured during normal driving.

4. Deep Transfer Learning for Traffic Sign Recognition

This paper explores the application of deep transfer learning for traffic sign recognition. The aim is to leverage the knowledge gained from a pre-existing dataset of traffic signs in a particular country/region to improve the recognition accuracy for traffic signs in another country/region using a deep learning framework. The use of deep transfer learning eliminates the need for data gathering and labelling for the target dataset. The study proposes three deep transfer learning methods, out of which two methods achieve significantly higher accuracy compared to a simple deep learning classifier. The results demonstrate that transferring knowledge between deep learning classifiers can enhance the accuracy of traffic sign recognition beyond the capabilities of a model that relies solely on deep learning for recognition.

5. A Survey on Road Traffic Sign Recognition System using Convolution Neural Network:

This paper presents a survey on road traffic sign recognition systems that utilize Convolutional Neural Network (CNN). Road traffic accidents are a major cause of death in India, and image recognition technology is being used to improve road safety. However, traditional artificial feature extraction techniques are time-consuming and complex. Therefore, researchers are working on improving the algorithms to make them more efficient and robust. The paper briefly introduces the principle of CNN and its applications in image processing. It also discusses the challenges faced by CNN in terms of time complexity and accuracy, and presents recent work aimed at improving efficiency. T

III. PROBLEM STATEMENT

In recent years, with the outbreak of Artificial Intelligence (AI), the vehicle-aided driving system has updated previous driving mode. Traffic sign recognition is a critical component of autonomous driving systemsand advanced driver assistance systems (ADAS). Traffic sign recognition is a critical component of modern transportation systems, essential for ensuring road safety, efficiency, and compliance with traffic regulations.As road networks continue to expand and evolve, the ability to accurately detect and interpret traffic signs becomes increasingly important.With the advent of autonomous vehicles and advanced driver assistance systems, the demand for reliable and efficient traffic sign recognition systems has never been greater.The increasing complexity of road networks and the need for efficient traffic management highlight the importance of robust and accurate traffic sign recognition systems. Traditional methods often struggle with variations in illumination, weather conditions, and sign occlusions, necessitating the development of more advanced techniques..

A. Requriments Tools

Software System Requirements:

1. Operating System: Windows XP/7/8/8.1/10, Linux and Mac
2. Coding Language: Python
3. Tools:

• CNN (Convolutional Neural Network)
• PyTorch
• TensorFlow/ Keras
• PIL(python Image Library)
• Sickit image
• opencv

IV. METHODOLOGY

Existing methodologies for traffic sign recognition often rely on handcrafted features and traditional classifiers, which may struggle with complex scenarios and variations in sign appearance. While some approaches achieve reasonable performance, they often lack the scalability and adaptability required for real-world applications. Therefore, there is a need for more advanced techniques that can leverage the power of deep learning to address these challenges effectively. Over the last decade, there has been a significant advancement in the development of intelligent transportation systems (ITS), particularly in the areas of advanced driver assistance systems (ADAS) and self-driving cars (SDC).However, the task of traffic sign detection and recognition remains a challenging problem for researchers and developers. It involves the detection, recognition, and classification of traffic signs using computer vision techniques. Most existing recognition algorithms have high accuracy rates but they have some disadvantages such as increased algorithmic complexity and heavy system hardware requirements. Different preprocessing for training data is also necessary.

V. EXPERIMENT RESULTS

Here we will get the traffic sign that can be recognized using deep learning.

VI. FUTURE ENHANCEMENT

To enhance a traffic sign recognition system using deep learning, consider focusing on both improving the model's architecture and expanding the dataset. Firstly, explore advanced deep learning architectures beyond basic CNNs, such as ResNet or EfficientNet, which can extract more complex features and improve the model's ability to generalize across different traffic sign variations and environmental conditions. Additionally, implement transfer learning techniques by fine-tuning pre-trained models on large-scale image datasets like ImageNet before adapting them to traffic sign recognition tasks. This approach can leverage learned features from general images to boost performance with limited traffic sign data.

Furthermore, invest efforts in dataset expansion and diversity. Collect additional annotated images representing a wide range of traffic signs under diverse conditions, including variations in lighting, weather, and viewpoints. Augment the dataset through techniques like image rotation, scaling, and color augmentation to expose the model to various scenarios it might encounter in real-world applications. By combining advanced architectures with a diverse and expanded dataset, the traffic sign recognition system can achieve improved accuracy, robustness, and generalization capability, paving the way for reliable deployment in practical settings such as autonomous vehicles or smart traffic management system

Conclusion

In conclusion, this project demonstrated the effectiveness of deep learning, specifically convolutional neural networks (CNNs), in recognizing and classifying traffic signs. By utilizing datasets such as the German Traffic Sign Recognition Benchmark (GTSRB), we were able to train and evaluate our models, achieving high accuracy rates on test data. The project highlighted the importance of data preprocessing, model selection, and hyperparameter tuning in improving the performance of the deep learning models. Techniques such as image augmentation, batch normalization, and dropout were crucial in reducing overfitting and improving generalization.

References

[1] Fatin Zaklouta and Bogdan Stanciulescu, ‘Robotics and Autonomous Systems, Real-time traffic recognization in three stages’, Robotics and Autonomous Systems 62(2014) 16-24. [2] P.G. Jimenez, S. Lafuente-Arroyo, H. Gomez-Moreno, F. Lopez-Ferreras, S. MaldonadoBascon, ‘Traffic sign shape classification evaluation’, part II, FFT applied to the signature of blobs, in Intelligent Vehicles Symposium, 2005, Proceedings, IEEE, IEEE, 2005, pp. 607– 612. [3] C. Fang, S. Chen, C. Fuh, ‘Road-sign detection and tracking’, IEEE Transactions on Vehicular Technology 52 (5) (2003) 1329–1341. [4] W.-J. Kuo, C.-C. Lin, ‘Two-stage road sign detection and recognition’, in 2007 IEEE International Conference on Multimedia and Expo, 2007, pp. 1427–1430. [5] Pierre Sermanet and Yann LeCun, ‘Traffic Sign Recognition with Multi-Scale Convolutional Networks’, Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 31 – August 5, 2011. [6] Yujun Zeng, Xin Xu, Yuqiang Fang, Kun Zhao, ‘Traffic Sign Recognition Using Extreme Learning Classifier with Deep Convolutional Features’. [7] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. ‘Gradient?based learning applied to document recognition’ .Proceedings of the IEEE, November 1998.

Copyright

Copyright © 2024 Mohit Kumar Sharma, Kunarapu Namratha, G. Kundanika, K. Kushal Kumar, P. Lakshitha Reddy, T. Tanish Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.