Complex Scene Segmentation Network Based on Multi-scale Encoding-decoding Architecture

Abstract

Abstract With the progress of artificial intelligence, the study of scene segmentation for complex scene understanding is of great significance. Due to the large number of activities, there are many target categories, large scale changes, many mutual occlusions, difficult target recognition, and large data labeling costs. In order to achieve accurate understanding of the complex scene, this paper proposes to add a scale adaptive feature module on the basis of Encode-Decode, so that the network can make good use of the features and context information of each level to effectively adapt to changes in target size. At the same time, we use the scale size function to weight encode different levels of features, which improves the prediction accuracy of pixels in the intersection area of each class. Experiments conducted on Cityscapes, Put_campus and PASCAL VOC 2012 datasets show that the method in this article is improved by about 1% compared with the three segmentation algorithms such as FCN, PSPNet, and Deeplabv3 +.