Convolutional neural network has achieved remarkable results in semantic segmentation tasks, because of its powerful learning ability. However, how to effectively use the low-level visual features and high-level semantic features of the network has been a research hotspot. Therefore, this paper proposed a semantic segmentation method that integrated multilevel feature information. This method extracted the features of each level through atrous convolution, iterated the deep features to enrich the low-level visual information, and finally merged with the high-level semantic features to obtain the fine semantic segmentation results. On the PASCAL VOC 2012 dataset, this method was compared with the five main methods. In the GTX1080Ti environment, the value of mIoU(mean banding-over-union) increased by 2.1% compared with the model whose performance was the second, and was only 0.4% lower than that of the model whose performance was the first. The result indicates that the proposed method can effectively make use of multi-level feature information, and the realize the purpose of image semantic segmentation.

[2020-11-05] Printed Article /doc/2019.07.0249/v2