CNN Model with Multilayer ASPP and Two-Step Cross-Stage for Semantic Segmentation

Abstract

Currently, interest in deep learning-based semantic segmentation is increasing in various fields such as the medical field, automatic operation, and object division. For example, UNet, a deep learning network with an encoder–decoder structure, is used for image segmentation in the biomedical area, and an attempt to segment various objects is made using ASPP such as Deeplab. A recent study improves the accuracy of object segmentation through structures that extend in various receptive fields. Semantic segmentation has evolved to divide objects of various sizes more accurately and in detail, and various methods have been presented for this. In this paper, we propose a model structure that reduces the overall parameters of the deep learning model in this development and improves accuracy. The proposed model is an encoder–decoder structure, and an encoder half scale provides a feature map with few encoder parameters. A decoder integrates feature maps of various scales with high area details and forward features of low areas. An integrated feature map learns a feature map of each encoder hierarchy over an area of previous data in the form of a continuous coupling structure. To verify the performance of the model, we learned and compared the KITTI-360 dataset with the Cityscapes dataset, and experimentally confirmed that the proposed method was superior to the existing model.