Real-time semantic segmentation network based on parallel atrous convolution for short-term dense concatenate and attention feature fusion-Reference-Cited by-同舟云学术

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Real-time semantic segmentation network based on parallel atrous convolution for short-term dense concatenate and attention feature fusion

Published:2024-04-10 Issue:3 Volume:21 Page:
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Wu Lijun,Qiu Shangdong,Chen Zhicong

Funder

The National Natural Science Foundation of China

The Foundation of Fujian Natural Science

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11554-024-01453-5.pdf

Reference48 articles.

1. Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)

2. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(04), 834–848 (2018)

3. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807 (2017)

4. Ding, P., Qian, H.: Light-deeplabv3+: a lightweight real-time semantic segmentation method for complex environment perception. J. Real-Time Image Proc. 21(1), 1 (2024)

5. Ding, P., Qian, H., Zhou, Y., Yan, S., Feng, S., Yu, S.: Real-time efficient semantic segmentation network based on improved aspp and parallel fusion module in complex scenes. J. Real-Time Image Proc. 20(3), 41 (2023)

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