Zhuang, Mingxi and Zhong, Xunyu and Gu, Dongbing and Feng, Liying and Zhong, Xungao and Hu, Huosheng (2021) LRDNet: A lightweight and efficient network with refined dual attention decorder for real-time semantic segmentation. Neurocomputing, 459. pp. 349-360. DOI https://doi.org/10.1016/j.neucom.2021.07.019
Zhuang, Mingxi and Zhong, Xunyu and Gu, Dongbing and Feng, Liying and Zhong, Xungao and Hu, Huosheng (2021) LRDNet: A lightweight and efficient network with refined dual attention decorder for real-time semantic segmentation. Neurocomputing, 459. pp. 349-360. DOI https://doi.org/10.1016/j.neucom.2021.07.019
Zhuang, Mingxi and Zhong, Xunyu and Gu, Dongbing and Feng, Liying and Zhong, Xungao and Hu, Huosheng (2021) LRDNet: A lightweight and efficient network with refined dual attention decorder for real-time semantic segmentation. Neurocomputing, 459. pp. 349-360. DOI https://doi.org/10.1016/j.neucom.2021.07.019
Abstract
Most of the current popular semantic segmentation convolutional networks are focus on accuracy and require large amount of computation, which is using complex models. In order to realize real-time performance in practical applications, such as embedded systems and mobile devices, lightweight semantic segmentation has become a new need, where the network model should keep good accuracy in very limited computing budget. In this paper, we propose a lightweight network with the refined dual attention decorder (termed LRDNet) for better balance between computational speed and segmentation accuracy. In the encoding part of LRDNet, we offer an asymmetric module based on the residual network for lightweight and efficiency. In this module, a combination of decomposition convolution and deep convolution is used to improve the efficiency of feature extraction. In the decoding part of LRDNet, we use a refined dual attention mechanism to reduce the complexity of the entire network. Our network attained precise real-time segmentation results on Cityscapes, CamVid datasets. Without additional processing and pretraining, the LRDNet model achieves 70.1 Mean IoU in the Cityscapes test set. With a parameter value below 0.66 M, it can be up to 77 FPS.
Item Type: | Article |
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Uncontrolled Keywords: | Lightweight semantic segmentation; Encoder-decoder; Residual network; Dual attention |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 19 Jul 2021 07:58 |
Last Modified: | 30 Oct 2024 19:17 |
URI: | http://repository.essex.ac.uk/id/eprint/30734 |
Available files
Filename: LRDNet.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0