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Classification of rare traffic signs

B.V. Faizov 1, V.I. Shakhuro 1, V.V. Sanzharov 1,3, A.S. Konushin 1,2

Lomonosov Moscow State University, Moscow, Russia,
NRU Higher School of Economics, Moscow, Russia,
Gubkin RSU of Oil and Gas

 PDF, 1210 kB

DOI: 10.18287/2412-6179-CO-601

Pages: 236-243.

Full text of article: Russian language.

The paper studies the possibility of using neural networks for the classification of objects that are few or absent at all in the training set. The task is illustrated by the example of classification of rare traffic signs. We consider neural networks trained using a contrastive loss function and its modifications, also we use different methods for generating synthetic samples for classification problems. As a basic method, the indexing of classes using neural network features is used. A comparison is made of classifiers trained with three different types of synthetic samples and their mixtures with real data. We propose a method of classification of rare traffic signs using a neural network discriminator of rare and frequent signs. The experimental evaluation shows that the proposed method allows rare traffic signs to be classified without significant loss of frequent sign classification quality.

traffic sign classification, synthetic training samples, neural networks, image recognition, image transforms, neural network compositions.

Faizov BV, Shakhuro VI, Sanzharov VV, Konushin AS. Classification of rare traffic signs. Computer Optics 2020; 44(2): 237-244. DOI: 10.18287/2412-6179-CO-601.

This work was supported by the Russian Science Foundation under RSF grant 18-31-20032 ("Physically correct lighting modeling and image synthesis on massively parallel computing systems in applications of artificial intelligence") and the Russian Science Foundation under RSF grant 17-71-20072 ("Deep Bayesian Methods in Machine Learning, Scalable Optimization and Computer Vision Problems").


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