From Synthetic Images Towards Detectors of Real Objects: A Case Study on Road Sign Detection

Klęsk, Przemysław

doi:10.1007/978-3-030-20915-5_2

Cited by 1 publication

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“…Detection and classification are the two essential steps involved in the ADAS system's functioning. There are many available options for selecting classification algorithms like Support Vector Machines (SVM) [3,4], Decision Tree (DT) [5], and Artificial Neural Networks (ANN) [6] that are considered traditional classifiers. Currently, the deep Convolutional Neural Network (CNN) is drawing much more attention from researchers as computationally more efficient [7].…”

Section: Introductionmentioning

confidence: 99%

Traffic Road Sign Recognition Using Multi-CNN Model for Autonomous Ground Vehicles

2024

European Economic Letters

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Detecting and recognizing the traffic road sign plays a significant role in autonomous vehicles while processing the details regarding each road signs. This research proposed a novel Multi Convolutional Neural Network (M-CNN) for recognizing different traffic signs. In this model, four different CNN structures are applied for processing various road signs. So, the high-level and low-level features are extracted to classify the signs into a proper class in driver-assisted autonomous vehicles. Here, the developed model has produced outstanding results while classifying different real-time traffic signs. The efficiency of the proposed model has been assessed by a well-known database named German Traffic Sign Recognition Benchmark (GTSRB). Moreover, the simulation outcomes demonstrate that the M-CNN model has certain advantages while detecting various traffic signs. Simulation results indicate that the proposed M-CNN model has a higher recognition rate and reduced processing time when compared to Machine Learning (ML) and Deep Learning (DL) models.

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Section: Introductionmentioning

confidence: 99%

Traffic Road Sign Recognition Using Multi-CNN Model for Autonomous Ground Vehicles

2024

European Economic Letters

View full text Add to dashboard Cite

show abstract

From Synthetic Images Towards Detectors of Real Objects: A Case Study on Road Sign Detection

Cited by 1 publication

References 10 publications

Traffic Road Sign Recognition Using Multi-CNN Model for Autonomous Ground Vehicles

Traffic Road Sign Recognition Using Multi-CNN Model for Autonomous Ground Vehicles

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