Using AAEHS-Net as an Attention-Based Auxiliary Extraction and Hybrid Subsampled Network for Semantic Segmentation.

Abstract

Semantic segmentation based on deep learning has undergone remarkable advancements in recent years. However, due to the neglect of the shallow features, the problems of inaccurate segmentation have persisted. To address this issue, a semantic segmentation network-attention-based auxiliary extraction and hybrid subsampled network (AAEHS-Net) is suggested in this study. To extract more deep information and the shallow features, the complementary and enhanced extraction module (CEEM) is utilized by the network. As a result, the edge segmentation of the model is improved. Moreover, to reduce the loss of features, a hybrid subsampled module (HSM) is introduced. Meanwhile, global max pool and global avg pool module (GAGM) is designed as an attention module to enhance the features with global and important information and maintain feature continuity. The proposed AAEHS-Net is evaluated on three datasets: the aerial drone image dataset, the Massachusetts roads dataset, and the Massachusetts buildings dataset. On the three datasets, AAEHS-Net achieves 1.15%, 0.88%, and 2.1% higher accuracy than U-Net, reaching 90.12%, 96.23%, and 95.15%, respectively. At the same time, our proposed network has obtained the best values for all evaluation metrics in three datasets compared to the currently popular algorithms.