Circular traffic sign recognition empowered by circle detection algorithm

Gunduz, Huseyin; Kaplan, Sevki Samet; Günal, Serkan; Akınlar, Cüneyt

doi:10.1109/siu.2013.6531432

Cited by 9 publications

(1 citation statement)

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“…TSD involves locating potential sign image regions from a natural scene image input. This initial stage is called the detection stage, in which a ROI-containing traffic sign is actually localized (Møgelmose, Trivedi & Moeslund, 2012;Gündüz, Kaplan & Günal, 2013). Traffic signs usually are of red, blue or white color and specific shapes like round, square, and triangular.…”

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

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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.

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Section: Introductionmentioning

confidence: 99%