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• W2779757316 abstract "In this paper, we propose a novel text-based traffic sign detection framework with two deep learning components. More precisely, we apply a fully convolutional network to segment candidate traffic sign areas providing candidate regions of interest (RoI), followed by a fast neural network to detect texts on the extracted RoI. The proposed method makes full use of the characteristics of traffic signs to improve the efficiency and accuracy of text detection. On one hand, the proposed two-stage detection method reduces the search area of text detection and removes texts outside traffic signs. On the other hand, it solves the problem of multi-scales for the text detection part to a large extent. Extensive experimental results show that the proposed method achieves the state-of-the-art results on the publicly available traffic sign data set: Traffic Guide Panel data set. In addition, we collect a data set of text-based traffic signs including Chinese and English traffic signs. Our method also performs well on this data set, which demonstrates that the proposed method is general in detecting traffic signs of different languages." @default.
• W2779757316 created "2018-01-05" @default.
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• W2779757316 date "2018-01-01" @default.
• W2779757316 modified "2023-10-18" @default.
• W2779757316 title "Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection" @default.
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• W2779757316 doi "https://doi.org/10.1109/tits.2017.2768827" @default.
• W2779757316 hasPublicationYear "2018" @default.
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