Brazilian Traffic Signs Detection and Recognition in Videos Using CLAHE, HOG Feature Extraction and SVM Cascade Classifier with Temporal Coherence

Junges, Renata Zottis; Paula, Mauricio Braga de; Aguiar, Marilton Sanchotene de

doi:10.1007/978-3-030-33749-0_47

Cited by 1 publication

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“…Building of Brazilian traffic sign datasets has been reported in [4,31], at least for model testing, but as far as we know, there is no Brazilian dataset publicly available. Taking into account this limitation and the above discussion regarding vegetation occlusion, the main contributions of the this paper are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Recognition with Deep Learning: Vegetation Occlusion Detection in Brazilian Environments

Dalborgo

Murari

Madureira³

et al. 2023

Sensors

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Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it’s possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies.

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Section: Introductionmentioning

confidence: 99%