中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Improved U-net for zebra-crossing image segmentation
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Improved U-net for zebra-crossing image segmentation
Jiahao Zhong; Qujiang Lei; Shangzhi Le; Xiangying Wei; Yuhe Wang; Weijun Wang; Wei Feng
2020
Conference Name2020 IEEE 6th International Conference on Computer and Communications (ICCC)
Conference Date2020
Conference Place四川
AbstractA safe crossing system is a prerequisite for improving the mobility of the visually impaired. For the blind, it is important to know exactly where the zebra crossings are. Zebra-crossing detection by machine vision can be a good solution to this problem. In this paper, we propose a model for fast and stable segmentation of crosswalks from captured images. For the blind, it is important to know exactly what area ahead is a zebra crossing. A common feature of all zebra crossings is the periodic appearance of white stripes on a black road. In this paper, we proposed a model for fast and stable segmentation of crosswalks from captured images. The model is improved based on U-net and consists of three steps. First, the input image is subsampled using ResNet-34's convolutional neural network to extract image features. Second, dilated convolution is used to increase the receptive field of feature points without decreasing the feature map resolution. Finally, the abstract features are restored to the original image size through the original up-sampling network of U-net with the complementary information of the skip connection.
Department南沙所-机器人中心
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/18356
Collection南沙所
Recommended Citation
GB/T 7714
Jiahao Zhong,Qujiang Lei,Shangzhi Le,et al. Improved U-net for zebra-crossing image segmentation[C],2020.
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