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• W3010199993 abstract "Semantic segmentation network is able to detect multi-scale traffic sign effectively. However, probabilities that different scale features participate in decision are equal in the network. In this work, we propose a local multi-scale attention module based on channel-attention, which extracts local features generated by dilated convolution and weights scale features. We demonstrate that adopting local multi-scale attention module on a semantic segmentation network achieves a better performance for locating multi-scale traffic signs than state-of-art convolutional networks by the experimental results on a Chinese traffic sign dataset." @default.
• W3010199993 created "2020-03-13" @default.
• W3010199993 creator A5010426520 @default.
• W3010199993 creator A5014158612 @default.
• W3010199993 creator A5060714978 @default.
• W3010199993 date "2019-10-01" @default.
• W3010199993 modified "2023-09-24" @default.
• W3010199993 title "Semantic Segmentation Network with Local Multi-Scale Attention for Locating Traffic Sign" @default.
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• W3010199993 doi "https://doi.org/10.1109/icicta49267.2019.00080" @default.
• W3010199993 hasPublicationYear "2019" @default.
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