关键词: 图像语义分割, DeeplabV3+, 高分辨率信息, 小目标分割

Abstract:

For the problems of small-scale target losing and discontinuous segmentation in existing image semantic segmentation, an image semantic segmentation algorithm with multi-scale feature fusion and enhancement is proposed. Based on DeeplabV3+ network, the algorithm improves the ability to describe small target features by building a multi-scale feature extraction and fusion enhancement network. The network can also obtain small target feature while segmenting large targets, so it can solve the problem of the small target losing and the discontinuous segmentation in the semantic segmentation. Experimental results on the Cityscapes dataset show that the improved algorithm significantly improves the accuracy of small target segmentation and optimizes the problem of discontinuous segmentation. Finally, the generalization of the improved algorithm is verified on the public dataset PASCAL VOC 2012.

Key words: image semantic segmentation, DeeplabV3+, high resolution characterizations, small target segmentation