Abstract：An end-to-end multi-scale semantic segmentation model based on fully convolutional DenseNet is proposed, aiming at the problems of traditional semantic segmentation methods for street scene, such as the large number of parameters and low computational efficiency and precision. Firstly, convolution layers embedded with hybrid dilation convolution are stacked to establish a dense module, and then the modules are cascaded along channel dimension to extract features. Next, multi-scale visual information regarded as supervised signals are transferred back to original channels. Finally, the prediction results are obtained by bilinear interpolation method. Experimental results on Cityscapes dataset demonstrate that the proposed method achieves an efficient segmentation and performs a better accuracy for street scene parsing.
蒋斌, 涂文轩, 杨超, 刘虹雨, 赵子龙. 基于DenseNet的复杂交通场景语义分割方法[J]. 模式识别与人工智能, 2019, 32(5): 472-480.
JIANG Bin1, TU Wenxuan1, YANG Chao1, LIU Hongyu1, ZHAO Zilong1. Semantic Segmentation Method for Complex Traffic Scene Based on DenseNet. , 2019, 32(5): 472-480.
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2019, Vol. 32 
