Speed limit traffic sign detection and recognition based on support vector machines

Ngoc, Hung; Vo, Minh-Thanh; Luong, Huy Quoc; Nguyen, An Hoang; Trang, Kien; Vu, Ly

doi:10.1109/atc.2017.8167633

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various machine learning (ML) and deep learning (DL) methods viz. support vector machines (SVM) (Agrawal & Chaurasiya, 2017;Do et al, 2017;Kiran, Prabhu, & Rajeev, 2009;Soni, Chaurasiya, & Agrawal, 2019), k-nearest neighbor (KNN) classifier (Sugiharto & Harjoko, 2016), fully convolutional network (FCN) (Y. , and convolutional neural network (CNN) (Aghdam, Heravi, & Puig, 2016;Farag & Saleh, 2018;Z. Zhu et al, 2016) have been employed for classification in TSR system.…”

Section: Introductionmentioning

confidence: 99%

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

Dubey

Chaurasiya

2022

International Journal of Cognitive Informatics and Natural Intelligence

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Dubey

Chaurasiya

2022

International Journal of Cognitive Informatics and Natural Intelligence

View full text Add to dashboard Cite

show abstract

“…The first study aims to detection and recognition of speed limit traffic sign using support vector machines [5]. This study presented a methodology for detection and recognition of speed limit traffic signs in Hochiminh City, Vietnam.…”

Section: Chapter II Literature Reviewmentioning

confidence: 99%

“…5, classification results are shown in terms of precision, recall, and f-measure of each class, and method accuracy. It shows that the overall accuracy of the proposed method is 93.98%.…”

mentioning

confidence: 99%

Thai traffic sign classification system

Namyang¹

View full text Add to dashboard Cite

Nowadays, driving assistance technology is continuously developed to serve a comfortable and safe driving experience for the driver. The traffic sign is an important feature to improve the ability of this technology. However, in general, the traffic sign has various structures and details in each specific country for a clearly understanding purpose. Thus, this thesis aims to propose the classification and recognition system for the Thai traffic sign. In this study, the classification process provides the ability to categorize the Thai traffic sign into four classes by using a Support Vector Machine (SVM) and Random Forest (RF) classifier with the combination of two descriptor features that are the Histogram of Oriented Gradients (HOG) and Color Layout Descriptor (CLD) feature. In this proposed classification technique, it is able to reach the accuracy up to 93.98%. Besides, this study also presents the recognition process to recognize the type of each traffic sign. This process using two main techniques: Optical Character Recognition (OCR) and Normalized Correlation Coefficient (NCCoef) template matching to predict the real meaning of each sign in their class after classifying them.

show abstract

Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features

Wei

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. At the first stage, we design an efficient network which is built with improved fire-modules to generate object proposals quickly. The network up-samples and merges the feature maps of different scales to attain a high-resolution fused feature map which contains semantically strong features of multi-scale objects. Specially, the prediction is made on the fuse feature map and based on the novel center-point estimation. With the overall designs, our region proposal network can achieve high recall value while using low-resolution images. At the second stage, a separate classification network is proposed. The bottleneck of the classification performance is generally caused by the greatly similar appearances between traffic signs. Therefore, we further explore local regions with critical differences between traffic signs to obtain fine-grained local features which help to improve classification. Finally, we evaluate our method on a challenge benchmark Tsinghua-Tencent 100K which provides many large images with small traffic sign instances. The experiment result shows that our method has better performance and faster detection speed than many state-of-the-art traffic sign detection methods. INDEX TERMS Traffic sign detection, multi-scale, center-point estimation, local features.

show abstract

Speed limit traffic sign detection and recognition based on support vector machines

Cited by 5 publications

References 10 publications

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

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

Thai traffic sign classification system

Traffic Sign Detection and Recognition Using Novel Center-Point Estimation and Local Features

Contact Info

Product

Resources

About