基于空洞空间金字塔池化的雾天图像语义分割.

Aiming at the problem of low segmentation accuracy in semantic segmentation of foggy images, this paper proposed a semantic segmentation method of foggy images that based on atrous space pyramid pooling, Xception module and residual network. On the one hand, this algorithm used the atrous space pyramid pooling and residual network to extract the multi-scale context features of the input image by the parallel convolution of multiple sampling rates and the convolution kernel size of 1 x 1. On the other hand, it used the decoder structure to classify the extracted features with Xception module after pretraining, and the prediction results of each pixel enhanced the refinement of the segmentation boundary and got the segmentation result with fine boundary. Experimental results show that the average intersection and union ratio of the proposed algorithm on foggy cityscapes dataset are 73. 03 %,73. 81 % and 74. 50%, and the segmentation performance is good. [ABSTRACT FROM AUTHOR]