Semantic segmentation based on DeepLabV3+ and superpixel optimization

Abstract

To tackle the problem where by DeepLabV3+ loses considerable detail information during feature extraction, which leads to poor segmentation results in the edges of the objects, this study proposed a semantics segmentation algorithm based on DeepLabV3+ and optimized by superpixels. First, a DeepLabV3+ model was chosen to extract semantic features and obtain coarse semantic segmentation results. Then, the simple linear iterative clustering algorithm was used to segment the input image into superpixels. Finally, high-level abstract semantic features and detailed information of the superpixels were fused to obtain edge optimized semantic segmentation results. Experiments conducted on the PASCAL VOC 2O12 dataset show that compared to DeepLabV3+, the proposed algorithm had superior performance in terms of detail parts such as edges of objects, and the value of mIoU reached 83.8%.The proposed algorithm thus outperformed other state-of-the-art algorithms in terms of semantic segmentation.