Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks

2014 ◽  
Vol 15 (5) ◽  
pp. 1991-2000 ◽  
Author(s):  
Junqi Jin ◽  
Kun Fu ◽  
Changshui Zhang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Hinge Loss

scholarly journals TRAFFIC SIGN RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS / KELIO ŽENKLŲ ATPAŽINIMAS NAUDOJANT NEURONINĮ TINKLĄ

2018 ◽  
Vol 10 (0) ◽  
pp. 1-5
Author(s):  
Ervin Miloš ◽  
Aliaksei Kolesau ◽  
Dmitrij Šešok
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Autonomous Driving ◽  
Histogram Equalization ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Computer Vision Applications

Traffic sign recognition is an important method that improves the safety in the roads, and this system is an additional step to autonomous driving. Nowadays, to solve traffic sign recognition problem, convolutional neural networks (CNN) can be adopted for its high performance well proved for computer vision applications. This paper proposes histogram equalization preprocessing (HOG) and CNN with additional operations – batch normalization, dropout and data augmentation. Several CNN architectures are compared to differentiate how each operation affects the accuracy of CNN model. Experimental results describe the effectiveness of using CNN with proposed operations. Santrauka Kelio ženklų atpažinimas – vienas iš svarbių būdų pagerinti saugumą keliuose. Ši sistema laikoma papildomu autonominio vairavimo žingsniu. Šiandien kelio ženklų atpažinimo problemai spręsti taikomi konvoliuciniai neuroniniai tinklai (KNN) dėl jų našumo, įrodyto vaizdų atpažinimo programose. Šiame straipsnyje siūlomas vaizdų histogramos išlyginimo apdorojimo metodas ir KNN su papildomomis operacijomis – paketo normalizavimas ir neuronų išjungimas / įjungimas. Yra palyginamos kelios KNN architektūros siekiant ištirti, kokią įtaką kiekviena operacija daro KNN modelio tikslumui. Eksperimentiniai rezultatai apibūdina KNN naudojimo efektyvumą su pasiūlytomis operacijomis.

Convolutional Neural Networks for Traffic Sign Recognition

2021 ◽  
Author(s):  
Zhonghua Wei ◽  
Heng Gu ◽  
Ran Zhang ◽  
Jingxuan Peng ◽  
Shi Qui
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition
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