Development of a deep wavelet pyramid scene parsing semantic segmentation network for scene perception in indoor environments

Abstract

In this paper, a new Deep Wavelet Pyramid Scene Parsing Network (DW-PSPNet) is proposed as an effective combination of Discrete Wavelet Transform (DWT), inception module, the channel and spatial attention modules, and PSPNet. Improved semantic segmentation via the combination, to our best knowledge, is not yet reported in the literature. The paper has two main contributions: (1) a new backbone network into PSPNET introduced by a combination of DWT, inspection modules, and attention mechanisms; (2) a new and improved version of PSPNet base structure. Further, three new modifications are introduced. First, the drop activation function is used to increase validation and test accuracy of the segmentation. Second, a skip connection from the backbone is applied to increase validation and test accuracies by restoring the resolution of feature maps via full utilization of multilevel semantic features. Third, Inverse Wavelet Transform (IWT) and convolution layer are applied to obtain the segmented images without information loss. DW-PSPNet was implemented via our own data generated by using a Robotis-Op3 humanoid robot to detect objects in indoor environments and and benchmark data set. Simulation results show higher performance of the proposed network compared with that of previous successful networks in handling semantic segmentation tasks in indoor environments. Moreover, extensive experiments on the benchmark Ade20K data set were also conducted. DW-PSPNET achieved an mIoU score of 45.97% on the ADE20K validation set, which are new state-of-the-art results. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.