关键词: 多层次特征融合, 上下文信息, 语义分割, 空洞卷积, 注意力机制

Abstract: Existing methods are difficult to achieve accurate results in object boundary regions due to multiple scales of objects, lack of high-level semantic abstraction information. To solve this problem, this paper proposes a semantic segmentation algorithm based on multi-level feature fusion. In decoding stages,  this paper designs three feature extraction branches, including a space detail branch, a semantic supplement branch, and a context information branch. In the space detail branch, this paper uses high-resolution shallow feature maps to directly generate a final segmentation map, which reserves a lot of spatial details. The semantic supplement branch is used to capture high-level semantic abstraction information. The context information branch is responsible for extracting multi-scale information. In the semantic supplement branch, this paper designs a feature fusion guidance module (FFGM) that can model the correspondence between pixels on different feature maps, thus features from different levels can be effectively fused. In the space detail branch, this paper proposes a self-enhancement module (SEM) to refine low-level features for obtaining clear boundary regions. In the context information branch, this paper uses a pyramidal pooling module (PPM) to achieve multi-scale context information for correcting misclassification of boundary pixels caused by multiple scales. Finally, attention mechanism is used to fuse features from the three branches for enhancing important features and suppressing indistinctive ones. Experimental results show that the proposed method obtains mIoU of 81.12% and 74.56% on PASCAL VOC2012 and Cityscapes datasets, respectively, and it obviously outperforms compared methods.

Key words: multi-level feature fusion, context information, semantic segmentation, atrous convolution, attention mechanism