Research on lightweight real-time semantic segmentation based on attention mechanism

Haojie Yu; Shijie Guan

doi:10.1117/12.2675290

25 May 2023 Research on lightweight real-time semantic segmentation based on attention mechanism

Haojie Yu, Shijie Guan

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126362F (2023) https://doi.org/10.1117/12.2675290
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Modern approaches to real-time semantic segmentation algorithms often sacrifice spatial resolution for real-time inference speed, which results in poor performance. Based on this, in this paper, we propose a lightweight real-time semantic segmentation network based on global attention mechanism by improving STDCNet. First, combined with asymmetric convolution, the short-term dense stitching module is light-weighted, and the global correlation of features is enhanced through global attention. Second, the edge branch can effectively filter out the boundary-independent information in the semantic features through the edge feature fusion module, and restore the lost detail information in the decoding stage. Finally, the improved loss function ensures that the network can update parameters in a direction that is conducive to small object segmentation. Tests and analysis on the Cityscapes dataset show that the lightweight contextual attention mechanism achieves 74.9% mIoU and 118.6FPS real-time semantic segmentation inference speed.

Citation Download Citation

Haojie Yu and Shijie Guan "Research on lightweight real-time semantic segmentation based on attention mechanism", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126362F (25 May 2023); https://doi.org/10.1117/12.2675290

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KEYWORDS

Semantics

Convolution

Atomic force microscopy

Design and modelling

Feature fusion

Network architectures

Feature extraction

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