Traffic sign recognition based on deep learning

Zhu, Yunyun; Yan, Wei Qi

doi:10.1007/s11042-022-12163-0

Cited by 111 publications

(43 citation statements)

References 29 publications

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“…As can be seen in Figure 18 , precision of 99.7% on our training set (blue line) was obtained. Please note that this precision is very similar to the results presented by other works, such as [ 54 , 55 ].…”

Section: Resultssupporting

confidence: 90%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings—an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision.

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Section: Resultssupporting

confidence: 90%

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Cucor

Petrov

Kamencay

et al. 2022

Sensors

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“…The GPU is recommended for network training by the Parallel Computing Toolbox. An object detection model called R-CNN [23], [33] uses. Traffic sign areas may be classified using Convolutional Neural Networks (CNN) [34].…”

Section: Methods Overviewmentioning

confidence: 99%

“…Thus, CNN's processing costs are decreased significantly. R-CNN [23], [33] is an object detection model that For sign and non-sign candidates, discriminative codewords are utilized to encode BoW histograms. Finally, the histograms are used to train an SVR model that can distinguish between candidates for signs and those who aren't, and weights are assigned depending on how well the candidates' intersection ratios match the real world.…”

Section: Methods Overviewmentioning

confidence: 99%

Traffic Sign Detection and Recognition Based on Convolutional Neural Network

Premamayudu¹

2022

IJRITCC

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As autonomous vehicles are developing and maturing the technology to implement the domestic autonomous vehicles. The critical technological problem for self-driving vehicles is traffic sign detection and recognition. A traffic sign recognition system is essential for an intelligent transportation system. The digital image processing techniques for object recognition and extraction of features from visual objects is a huge process and include many conversions and pre-processing steps. A deep learning-based convolutional neural network (CNN) model is one of the suitable approach for traffic sign detection and recognition. This model has overcome significant shortcomings of traditional visual object detection approaches. This paper proposed a traffic sign identification and detection system. The proposed design and strategy are implemented using the Tensorflow framework in google colab environment. The experiment is applied on the publicly available traffic sign data sets. The defined deep convolution neural network based model experimental results achieved 94.52% and 80.85% precision and recall respectively. Improving the seep of recognition and identifying appropriate features of traffic sign objects are addressed using deep learning-based encoders and transformers.

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“…The aforementioned class incremental setting could also be used to build adaptive models to the challenging changing environment that autonomous driving systems face. We specifically address the traffic sign recognition problem [10][11][12][13]. This task is of special interest because, in the case of traffic signs, the semantic taxonomy is strongly related to the visual appearance.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Novelty Detection for Traffic Sign Recognition

Ruiz

Serrat

2022

Sensors

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Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy.

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Traffic sign recognition based on deep learning

Cited by 111 publications

References 29 publications

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

Traffic Sign Detection and Recognition Based on Convolutional Neural Network

Hierarchical Novelty Detection for Traffic Sign Recognition

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