DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary

• URL: http://arxiv.org/abs/2111.00509v1
• Date: Sun, 31 Oct 2021 14:20:02 GMT
• Title: DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary
• Authors: Linjie Wang, Quan Zhou, Chenfeng Jiang, Xiaofu Wu, and Longin Jan Latecki
• Abstract summary: This paper introduces a lightweight dual-resolution network, called DRBANet, aiming to refine semantic segmentation results with the aid of boundary information. DRBANet adopts dual parallel architecture, including: high resolution branch (HRB) and low resolution branch (LRB) Experiments on Cityscapes and CamVid datasets demonstrate that our method achieves promising trade-off between segmentation accuracy and running efficiency.
• Score: 15.729067807920236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the benefit of boundary information. This paper introduces a lightweight dual-resolution network, called DRBANet, aiming to refine semantic segmentation results with the aid of boundary information. DRBANet adopts dual parallel architecture, including: high resolution branch (HRB) and low resolution branch (LRB). Specifically, HRB mainly consists of a set of Efficient Inverted Bottleneck Modules (EIBMs), which learn feature representations with larger receptive fields. LRB is composed of a series of EIBMs and an Extremely Lightweight Pyramid Pooling Module (ELPPM), where ELPPM is utilized to capture multi-scale context through hierarchical residual connections. Finally, a boundary supervision head is designed to capture object boundaries in HRB. Extensive experiments on Cityscapes and CamVid datasets demonstrate that our method achieves promising trade-off between segmentation accuracy and running efficiency.

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