Отрывок: We have used the pre-trained method ’Fast Semantic Seg- mentation’ [39]. It generates plausible semantic segmen- tation. We have used the obtained semantic maps to train where module of the neural network for object placement. After that, we have used a trained neural network to sample the locations and sizes of new traffic signs. When generating them, we have made sure that the new exam- ples did not overlap. The number of traffic signs for each image has been determined usi...
Название : | Road images augmentation with synthetic traffic signs using neural networks |
Авторы/Редакторы : | Konushin, A.S. Faizov, B.V. Shakhuro, V.I. |
Ключевые слова : | traffic sign classification synthetic training samples neural networks image recognition image transforms neural network compositions |
Дата публикации : | Сен-2021 |
Издательство : | Самарский национальный исследовательский университет |
Библиографическое описание : | Konushin AS, Faizov BV, Shakhuro VI. Road images augmentation with synthetic traffic signs using neural networks. Computer Optics 2021; 45(5): 736-748. DOI: 10.18287/2412-6179-CO-859. |
Серия/номер : | 45;5 |
Аннотация : | Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector. |
URI (Унифицированный идентификатор ресурса) : | 10.18287/2412-6179-CO-859 http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Road-images-augmentation-with-synthetic-traffic-signs-using-neural-networks-91895 |
Другие идентификаторы : | Dspace\SGAU\20211010\91895 |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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Файл | Описание | Размер | Формат | |
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13_Konushin-Faizov-Shakhuro_SV(Pics)-KI-JuN-Lit-MA-JuN2-!.pdf | Основная статья | 5.81 MB | Adobe PDF | Просмотреть/Открыть |
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