Image semantic segmentation model based on CBAMUNet

Qian Guo; Yanlong Xu

doi:10.1117/12.3025266

21 February 2024 Image semantic segmentation model based on CBAMUNet

Qian Guo, Yanlong Xu

Proceedings Volume 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024); 130800C (2024) https://doi.org/10.1117/12.3025266
Event: International Conference on Frontiers of Applied Optics and Computer Engineering, 2024, Kunming, China

Abstract

Mainstream image semantic segmentation networks can only extract local features of an image. Their receptive field is limited to the range of convolutional kernel size, which impacts the distinguishability of extracted features by the encoder. This issue leads to inaccurate segmentation, loss of small targets, and other related problems. This paper presents a solution to the aforementioned challenges by proposing the use of a CBAMUNet image semantic segmentation model. The model combines CBAM attention module and UNet En-decoder network. Firstly, the UNet network has been enhanced to eliminate the need for cropping of input images to reduce feature loss. Secondly, the CBAM module has been integrated into its skip connection to enable the UNet network to extract more efficient features and enhance the segmentation effect. And the model uses a new loss function. Data augmentation is also used to improve the generalization ability of the model. Through the comparison experiments with UNet, it is found that CBAMUNet is able to significantly improve the segmentation effect of images, and increase the pixel precision(PA) and the mean intersection over union(mIoU) of the semantic segmentation of images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Guo and Yanlong Xu "Image semantic segmentation model based on CBAMUNet", Proc. SPIE 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024), 130800C (21 February 2024); https://doi.org/10.1117/12.3025266

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KEYWORDS

Image segmentation

Semantics

Data modeling

Education and training

Feature extraction

Visualization

Performance modeling

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