Towards Real-Time Traffic Sign Detection and Classification

Yang, Yi; Luo, Huiwu; Xu, Haisong; Wu, Fuchao

doi:10.1109/tits.2015.2482461

Cited by 266 publications

(111 citation statements)

References 24 publications

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“…In addition, the test set of CTSD in Reference [6] contains 139 mandatory signs, 264 prohibitory signs, and 129 danger signs. Concerning the test set for CCTSDB-excepting tiny traffic signs that are not clear enough to divide into subclasses-we found that the test set contained a total of 209 mandatory signs, 376 prohibitory signs, and 151 danger signs.…”

Section: Chinese Traffic Sign Datasetmentioning

confidence: 99%

“…The methods of References [6,9,11,12] are based on the traditional detection algorithms. As shown in Table 6, although our method was only better than the method in Reference [9], the running time proved to be fastest.…”

Section: Evaluations On Gtsdbmentioning

confidence: 99%

“…For researching Chinese traffic sign detection methods, there is a recently published Chinese traffic sign dataset (CTSD) [6]. Figures 2 and 3 show some samples of the three super categories in Germany and China, respectively.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

et al. 2017

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Abstract:Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.

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Section: Chinese Traffic Sign Datasetmentioning

confidence: 99%

Section: Evaluations On Gtsdbmentioning

confidence: 99%

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A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

et al. 2017

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“…The completeness of results obtained for the diagnosis and classification steps implies that method is encouraging for the program recognition and inventory of traffic indicators in street mapping applications. [2] in article towards real-time traffic sign detection and classification they focused on popularity taken on an important role in drivers associated systems and wise autonomous vehicles. It is real-time performance is appealing in addition to its recognition performance highly.…”

Section: Literature Surveymentioning

confidence: 99%

“…STS systems are being greatly deployed to increase traveling safeness and efficiency [1]. It aims to manage factors that are typically at odds with the other person such as vehicles, routes, and loads to boost safety and reduce vehicle wear, transportation times and fuel prices [2].…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Traffic Signs using Machine Learning Techniques

Rai¹,

Pachlasiya²

2017

IJCA

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The computerized recognition and classification of traffic signs is a challenging problem, with several important request areas, including advanced drivers assistance systems, autonomous vehicles and street surveying. While much research is present on both automated diagnosis and popularity of symbol-based traffic indicators there is much less research concentrated specifically on the reputation of wording on traffic information indications. This may be partial because of the difficulty of the duty brought on by problems, such as brightness and shadows, blurring, occlusion, and signal deterioration. Our method of this issue by detecting many text-based traffic indication prospects using basic condition and color information. The proposed system includes two main periods: Recognition and Classification. The Acceptance stage exploits the understanding of the composition of the Traffic indication, i.e., the condition and size of the sign in the frame, to look for the locations in the landscape that it will seek out traffic text indications.

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Spatial–temporal feature extraction based on convolutional neural networks for travel time prediction

Chen

Yang

2022

Applied Research

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In recent years, some traffic information prediction methods have been proposed to provide the precise information on travel time, vehicle speed and traffic flow for highways. However, huge errors may be obtained by these methods for urban roads or alternative roads of highways. Therefore, this study proposes a travel time prediction method based on convolutional neural networks to extract important factors for the improvement of traffic information prediction. In practical experimental environments, the travel time records on No. 5 Highway and its alternative roads were collected and used to evaluate the proposed method. The results showed that the mean absolute percentage error of the proposed method was about 5.69%. Therefore, the proposed method based on deep-learning techniques can improve the accuracy of travel time prediction.

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Towards Real-Time Traffic Sign Detection and Classification

Cited by 266 publications

References 24 publications

A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

Recognition and Classification of Traffic Signs using Machine Learning Techniques

Spatial–temporal feature extraction based on convolutional neural networks for travel time prediction

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