JACIII Vol.27 No.4 pp. 673-682
doi: 10.20965/jaciii.2023.p0673

Research Paper:

Lightweight Bilateral Network for Real-Time Semantic Segmentation

Pengtao Wang ORCID Icon, Lihong Li ORCID Icon, Feiyang Pan ORCID Icon, and Lin Wang ORCID Icon

School of Information and Electrical Engineering, Hebei University of Engineering
No.19 Taiji Road, Handan, Hebei 056038, China

Corresponding author

February 11, 2023
April 4, 2023
July 20, 2023
real-time semantic segmentation, depth separable convolution, attention mechanism

Herein, a dual-branch semantic segmentation model based on depth-separable convolution and attention mechanism is proposed for the real-time and accuracy requirement of semantic segmentation. The proposed approach overcomes the problems of poor segmentation effect and over-simplification of feature fusion arising from the constant downsample operations in semantic segmentation. The network is divided into spatial detail and semantic information paths. The spatial detail path utilizes a smaller downsample multiplier to maintain resolution and efficiently extract spatial information. The semantic information path is constructed by a non-bottleneck residual unit with dilated convolution; it extracts semantic features. For the feature aggregation problem, the feature-guided fusion module is designed to assign different weights to the parts of the two paths and fuse them to obtain the final output. The proposed algorithm achieves a segmentation accuracy of 69.6% and speed of 70 fps on the Cityscapes dataset, with a model parameter count of only 0.76 M, thus indicating some advantages over recent real-time semantic segmentation algorithms. The proposed method with depth separable convolution and attention mechanism can effectively extract features and compensate for the loss of accuracy caused by downsampling. The experiments demonstrate that the proposed fusion module outperforms other methods in fusing different features.

Cite this article as:
P. Wang, L. Li, F. Pan, and L. Wang, “Lightweight Bilateral Network for Real-Time Semantic Segmentation,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 673-682, 2023.
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