EFRNet: A Lightweight Network with Efficient Feature Fusion and Refinement for Real-Time Semantic Segmentation

To pursue high accuracy, most image semantic segmentation methods are computationally costly and thus not suitable to real-time applications. Existing lightweight methods either adopt a single branch without feature fusion, which dam-ages accuracy, or introduce extra branches for feature fusion, whi...

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Bibliographic Details
Main Authors: Zhang, Kuayue, Liao, Qingmin, Zhang, Juncheng, Liu, Shaojun, Ma, Haoyu, Xue, Jing-Hao
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Data mining
deep convolutional neural network
deep learning
Feature extraction
Fuses
Image segmentation
Real-time
Real-time systems
semantic segmentation
Semantics
Online Access:Request full text
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Summary:To pursue high accuracy, most image semantic segmentation methods are computationally costly and thus not suitable to real-time applications. Existing lightweight methods either adopt a single branch without feature fusion, which dam-ages accuracy, or introduce extra branches for feature fusion, which harms efficiency. In this paper, we propose a lightweight network named EFRNet, with feature fusion and refinement in a single branch to achieve better balance between accuracy and efficiency in real-time semantic segmentation. Specifically, in EFRNet, we design a novel Feature Fusion Module to fuse multi-stage features in a single CNN efficiently, and we propose a lightweight Channel Attention Refinement Module to refine features with few extra parameters. Extensive experiments show that our EFRNet achieves decent accuracy with an extremely small model size and high inference speed. It achieves the best accuracy of 70.02% mIoU compared with state-of-the-art lightweight methods on CamVid with only 0.48M parameters.
ISSN:1945-788X
DOI:10.1109/ICME51207.2021.9428371