scholarly journals Eigen-Gradients for Traffic Sign Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Sheila Esmeralda Gonzalez-Reyna ◽  
Juan Gabriel Avina-Cervantes ◽  
Sergio Eduardo Ledesma-Orozco ◽  
Ivan Cruz-Aceves
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Autonomous Driving ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Road Sign ◽  
Driver Support Systems

Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.

scholarly journals Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2684 ◽  
Author(s):  
Obed Tettey Nartey ◽  
Guowu Yang ◽  
Sarpong Kwadwo Asare ◽  
Jinzhao Wu ◽  
Lady Nadia Frempong
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Unbalanced Data ◽  
Traffic Sign Recognition ◽  
Training Set ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Weakly Supervised

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications.

Autonomous Vehicle Application for Improving Traffic Sign Learning near Ramps

2020 ◽  
Vol 2674 (10) ◽  
pp. 158-166
Author(s):  
Zhenhua Zhang ◽  
Leon Stenneth ◽  
Xiyuan Liu
Keyword(s):  
State Of The Art ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Speed Reduction ◽  
Sign Recognition ◽  
Path Information ◽  
Real World Applications ◽  
Location Adjustment

The state-of-the-art traffic sign recognition (TSR) algorithms are designed to recognize the textual information of a traffic sign at over 95% accuracy. Even though, they are still not ready for complex roadworks near ramps. In real-world applications, when the vehicles are running on the freeway, they may misdetect the traffic signs for the ramp, which will become inaccurate feedback to the autonomous driving applications and result in unexpected speed reduction. The misdetection problems have drawn minimal attention in recent TSR studies. In this paper, it is proposed that the existing TSR studies should transform from the point-based sign recognition to path-based sign learning. In the proposed pipeline, the confidence of the TSR observations from normal vehicles can be increased by clustering and location adjustment. A supervised learning model is employed to classify the clustered learned signs and complement their path information. Test drives are conducted in 12 European countries to calibrate the models and validate the path information of the learned sign. After model implementation, the path accuracy over 1,000 learned signs can be increased from 75.04% to 89.80%. This study proves the necessity of the path-based TSR studies near freeway ramps and the proposed pipeline demonstrates a good utility and broad applicability for sensor-based autonomous vehicle applications.

scholarly journals TRAFFIC SIGN RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS / KELIO ŽENKLŲ ATPAŽINIMAS NAUDOJANT NEURONINĮ TINKLĄ

2018 ◽  
Vol 10 (0) ◽  
pp. 1-5
Author(s):  
Ervin Miloš ◽  
Aliaksei Kolesau ◽  
Dmitrij Šešok
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Autonomous Driving ◽  
Histogram Equalization ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Computer Vision Applications

Traffic sign recognition is an important method that improves the safety in the roads, and this system is an additional step to autonomous driving. Nowadays, to solve traffic sign recognition problem, convolutional neural networks (CNN) can be adopted for its high performance well proved for computer vision applications. This paper proposes histogram equalization preprocessing (HOG) and CNN with additional operations – batch normalization, dropout and data augmentation. Several CNN architectures are compared to differentiate how each operation affects the accuracy of CNN model. Experimental results describe the effectiveness of using CNN with proposed operations. Santrauka Kelio ženklų atpažinimas – vienas iš svarbių būdų pagerinti saugumą keliuose. Ši sistema laikoma papildomu autonominio vairavimo žingsniu. Šiandien kelio ženklų atpažinimo problemai spręsti taikomi konvoliuciniai neuroniniai tinklai (KNN) dėl jų našumo, įrodyto vaizdų atpažinimo programose. Šiame straipsnyje siūlomas vaizdų histogramos išlyginimo apdorojimo metodas ir KNN su papildomomis operacijomis – paketo normalizavimas ir neuronų išjungimas / įjungimas. Yra palyginamos kelios KNN architektūros siekiant ištirti, kokią įtaką kiekviena operacija daro KNN modelio tikslumui. Eksperimentiniai rezultatai apibūdina KNN naudojimo efektyvumą su pasiūlytomis operacijomis.

scholarly journals Evaluation of Robust Spatial Pyramid Pooling Based on Convolutional Neural Network for Traffic Sign Recognition System

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 889 ◽  
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Shao-Kuo Tai
Keyword(s):  
Autonomous Driving ◽  
Recognition System ◽  
Main Role ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Average Accuracy ◽  
Spatial Pyramid Pooling ◽  
Object Features ◽  
Spatial Pyramid

Traffic sign recognition (TSR) is a noteworthy issue for real-world applications such as systems for autonomous driving as it has the main role in guiding the driver. This paper focuses on Taiwan’s prohibitory sign due to the lack of a database or research system for Taiwan’s traffic sign recognition. This paper investigates the state-of-the-art of various object detection systems (Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3) combined with spatial pyramid pooling (SPP). We adopt the concept of SPP to improve the backbone network of Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3 for building feature extraction. Furthermore, we use a spatial pyramid pooling to study multi-scale object features thoroughly. The observation and evaluation of certain models include vital metrics measurements, such as the mean average precision (mAP), workspace size, detection time, intersection over union (IoU), and the number of billion floating-point operations (BFLOPS). Our findings show that Yolo V3 SPP strikes the best total BFLOPS (65.69), and mAP (98.88%). Besides, the highest average accuracy is Yolo V3 SPP at 99%, followed by Densenet SPP at 87%, Resnet 50 SPP at 70%, and Tiny Yolo V3 SPP at 50%. Hence, SPP can improve the performance of all models in the experiment.

scholarly journals Efficient Traffic Sign Recognition Using CLAHE-Based Image Enhancement and ResNet CNN Architectures

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Image Enhancement ◽  
Autonomous Driving ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
The Road ◽  
Sign Recognition ◽  
On The Road

Recognition and classification of traffic signs and other numerous displays on the road are very crucial for autonomous driving, navigation, and safety systems on roads. Machine learning or deep learning methods are generally employed to develop a traffic sign recognition (TSR) system. This paper proposes a novel two-step TSR approach consisting of contrast limited adaptive histogram equalization (CLAHE)-based image enhancement and convolutional neural network (CNN) as multiclass classifier. Three CNN architectures viz. LeNet, VggNet, and ResNet were employed for classification. All the methods were tested for classification of German traffic sign recognition benchmark (GTSRB) dataset. The experimental results presented in the paper endorse the capability of the proposed work. Based on experimental results, it has also been illustrated that the proposed novel architecture consisting of CLAHE-based image enhancement & ResNet-based classifier has helped to obtain better classification accuracy as compared to other similar approaches.

scholarly journals A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet-5 Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ameur Zaibi ◽  
Anis Ladgham ◽  
Anis Sakly
Keyword(s):  
Deep Learning ◽  
Research Work ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Data Set ◽  
Road Signs ◽  
Sign Recognition ◽  
Road Sign ◽  
Recognition Systems ◽  
Embedded Application

For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet-5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet-5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.

Augmented Reality Aspects of Object Recognition in Driver Support Systems

2012 ◽  
Vol 16 (2) ◽  
pp. 284-296 ◽  
Author(s):  
Zsombor Paróczi ◽  
◽  
István Nagy ◽  
Csaba Gáspár-Papanek ◽  
Zsolt T. Kardkovács ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Car Traffic ◽  
Driver Support Systems ◽  
Independent Work ◽  
Driver Support

Augmented Reality (AR) is a technique that combines a live view in real-time with virtual computergenerated images, creating a real-time augmented experience of reality. In this paper we define connection between augmented reality and cognitive infocommunications and show a demonstration system for driver support which combines raw sensory with extracted visual data to provide extended information about the visual scene around the car. Traffic sign recognition system presented in this paper is an independent work of U1 Research and Top-Map Plc.

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