Abstract
AbstractThe traditional complete dual-branch structure is effective for semantic segmentation tasks. However, it is redundant in some sense. Moreover, the simple additive fusion of the features from the two branches may not achieve the satisfactory performance. To alleviate these two problems, in this paper we propose an efficient compact interactive dual-branch network (CIDNet) for real-time semantic segmentation. Specifically, we first build a compact interactive dual-branch structure by constructing a compact detail branch and a semantic branch. Furthermore, we build a detail-semantic interactive module to fuse several specific stages of the two branches in the backbone network with the corresponding stages of the detail resolution branch. Finally, we propose a dual-branch contextual attention fusion module to deeply fuse the extracted features and predict the final segmentation result. Extensive experiments on Cityscapes and CamVid dataset demonstrate that the proposed CIDNet achieve satisfactory trade-off between segmentation accuracy and inference speed, and outperforms 20 representative real-time semantic segmentation methods.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence
Reference58 articles.
1. Tao H, Qiu J, Chen Y, Stojanovic V, Cheng L (2023) Unsupervised cross-domain rolling bearing fault diagnosis based on time–frequency information fusion. J Frankl Inst 360(2):1454–1477
2. Dong Y, Jiang Z, Tao F, Fu Z (2022) Multiple spatial residual network for object detection. Complex Intell Syst 2022:1–16
3. Dong Y, Shen L, Pei Y, Yang H, Li X (2023) Field-matching attention network for object detection. Neurocomputing 535:123–133
4. Dong Y, Tan W, Tao D, Zheng L, Li X (2021) Cartoonlossgan: learning surface and coloring of images for cartoonization. IEEE Trans Image Process 31:485–498
5. Zhuang Z, Tao H, Chen Y, Stojanovic V, Paszke W (2022) An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints. IEEE Trans Syst Man Cybern Syst 2022:1
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