scholarly journals Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4021 ◽  
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Silun Peng ◽  
Feng Xiao ◽  
Shixin Song
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Intelligent Vehicles ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Hardware Efficient Modified CNN Architecture for Traffic Sign Detection and Recognition

2021 ◽  
pp. 2250017
Author(s):  
Bhaumik Vaidya ◽  
Chirag Paunwala
Keyword(s):  
Feature Fusion ◽  
Detection Accuracy ◽  
Field Calculation ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Adverse Weather ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic sign recognition is a vital part for any driver assistance system which can help in making complex driving decision based on the detected traffic signs. Traffic sign detection (TSD) is essential in adverse weather conditions or when the vehicle is being driven on the hilly roads. Traffic sign recognition is a complex computer vision problem as generally the signs occupy a very small portion of the entire image. A lot of research is going on to solve this issue accurately but still it has not been solved till the satisfactory performance. The goal of this paper is to propose a deep learning architecture which can be deployed on embedded platforms for driver assistant system with limited memory and computing resources without sacrificing on detection accuracy. The architecture uses various architectural modification to the well-known Convolutional Neural Network (CNN) architecture for object detection. It uses a trainable Color Transformer Network (CTN) with the existing CNN architecture for making the system invariant to illumination and light changes. The architecture uses feature fusion module for detecting small traffic signs accurately. In the proposed work, receptive field calculation is used for choosing the number of convolutional layer for prediction and the right scales for default bounding boxes. The architecture is deployed on Jetson Nano GPU Embedded development board for performance evaluation at the edge and it has been tested on well-known German Traffic Sign Detection Benchmark (GTSDB) and Tsinghua-Tencent 100k dataset. The architecture only requires 11 MB for storage which is almost ten times better than the previous architectures. The architecture has one sixth parameters than the best performing architecture and 50 times less floating point operations per second (FLOPs). The architecture achieves running time of 220[Formula: see text]ms on desktop GPU and 578 ms on Jetson Nano which is also better compared to other similar implementation. It also achieves comparable accuracy in terms of mean average precision (mAP) for both the datasets.

A Review and Comparative Analysis of Recent Advancements in Traffic Sign Detection and Recognition Techniques

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Khyati Chourasia ◽  
Jitendra N. Chourasia
Keyword(s):  
Template Matching ◽  
Safety Information ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Fuzzy Classifier ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

This paper presents a comprehensive study of the automatic detection and recognition of traffic sign. The object of this review is to reduce the search for quality Traffic sign recognition system and to indicate the potential regions for increasing the efficiency, accuracy and speed of the system. The traffic sign carry the very important and valuable safety information through the peculiar characteristics. Different categories of traffic sign with their characteristics are presented. The practical difficulty that arises in actual time traffic sign is summarized. It describes also the techniques used for the detection, recognition and classification of the traffic signs. The traffic sign detection using color and shape detection are most commonly used. Some authors also used adaboost detector and decision tree method for detection. Most of the researcher used different type of Neural Network for recognition and classification. Some of the authors used fuzzy classifier and genetic algorithm. Template matching and model based method is also used for classification. A lot of improvements are still required for development efficient, fast, robustness traffic sign recognition system.

scholarly journals New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3776 ◽  
Author(s):  
Jameel Khan ◽  
Donghoon Yeo ◽  
Hyunchul Shin
Keyword(s):  
Recognition Performance ◽  
Recognition System ◽  
Tone Mapping ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Sign Detection ◽  
Illumination Preprocessing ◽  
Traffic Sign Detection

In this paper, we propose a new Intelligent Traffic Sign Recognition (ITSR) system with illumination preprocessing capability. Our proposed Dark Area Sensitive Tone Mapping (DASTM) technique can enhance the illumination of only dark regions of an image with little impact on bright regions. We used this technique as a pre-processing module for our new traffic sign recognition system. We combined DASTM with a TS detector, an optimized version of YOLOv3 for the detection of three classes of traffic signs. We trained ITSR on a dataset of Korean traffic signs with prohibitory, mandatory, and danger classes. We achieved Mean Average Precision (MAP) value of 90.07% (previous best result was 86.61%) on challenging Korean Traffic Sign Detection (KTSD) dataset and 100% on German Traffic Sign Detection Benchmark (GTSDB). Result comparisons of ITSR with latest D-Patches, TS detector, and YOLOv3 show that our new ITSR significantly outperforms in recognition performance.

scholarly journals Traffic Sign Detection Based on Convolutional Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 2926-2934
Author(s):  
Arjun Dileep
Keyword(s):  
Recognition Rate ◽  
Vital Role ◽  
Motive Force ◽  
Image Process ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Help Systems ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

DETEKSI DAN REKOGNISI RAMBU-RAMBU LALU LINTAS DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Kurniawan Nur Ramadhani ◽  
M.Syahrul Mubarok ◽  
Agnes Dirgahayu Palit
Keyword(s):  
Support Vector Machines ◽  
Road Safety ◽  
Support Vector ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Vector Machines ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

scholarly journals Traffic Sign Detection and Recognition

2020 ◽  
Vol 5 (7) ◽  
pp. 1143-1146
Author(s):  
Anju C P ◽  
Andria Joy ◽  
Haritha Ashok ◽  
Joseph Ronald Pious ◽  
Livya George
Keyword(s):  
Language Processing ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Textual Data ◽  
Sign Detection ◽  
Local Residents ◽  
Non Local ◽  
Traffic Sign Detection ◽  
Detection And Recognition

As placement of traffic sign board do not follow any international standard, it may be difficultfor non-local residents to recognize and infer the signs easily. So, this project mainly focuses ondemonstrating a system that can help facilitate this inconvenience. This can be achieved byinterpreting the traffic sign as a voice note in the user’s preferred language. Therefore, the wholeprocess involves detecting the traffic sign, detecting textual data if any with the help of availabledatasets and then processing it into an audio as the output to the user in his/her preferred language.The proposed system not only tackles the above-mentioned problem, but also to an extent ensuressafer driving by reducing accidents through conveying the traffic signs properly. The techniques usedto implement the system include digital image processing, natural language processing and machinelearning concepts. The implementation of the system includesthree major steps which are detection of traffic sign from a captured traffic scene, classification of traffic signs and finally conversion of classified traffic signs to audio message.

scholarly journals Traffic Sign Detection and Recognition using Image Processing

2021 ◽  
Vol 9 (12) ◽  
pp. 1059-1063
Author(s):  
Mr. Mohammad Shabbir Sheikh
Keyword(s):  
Gabor Filter ◽  
Learning Technology ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Detection And Identification ◽  
Sign Detection ◽  
On The Road ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: Now a days, automobiles became most convenient mode of transportation for everyone. As we know one of the most important functions, TSDR has become a popular research . It primarily involves the use of vehicle cameras to collect real- time road pictures and then recognize and identify traffic signs seen on the road, therefore delivering correct data to the driving system. With the advancement of science and technology, an increasing number of scholars are turning to deep learning technology to save time in traditional processes. From the training samples, this model can learn the deep features inside the autonomously. The accuracy and great efficiency of detection and identification are the subject of this essay. A deep convolution neural network algorithm is proposed to train traffic sign training sets using Caffe[3], an open-source framework, in order to obtain a model that can classify traffic signs and learn and identify the most critical of these traffic sign features, in order to achieve the goal of identifying traffic signs in the real world. Keywords: Traffic sign, Segmentation, Gabor filter, Traffic Sign Detection and Recognition (TSDR)

scholarly journals Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3192 ◽  
Author(s):  
Faming Shao ◽  
Xinqing Wang ◽  
Fanjie Meng ◽  
Ting Rui ◽  
Dong Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Gabor Wavelets ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Driving ◽  
Real Time Traffic ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition ◽  
Grayscale Images

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

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