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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation.

Xunpeng Yi¹, Haonan Zhang¹, Yibo Wang¹

¹Electronic Information School, Wuhan University, Wuhan 430064, China.

Sensors (Basel, Switzerland)

|August 26, 2022

Summary

This study introduces a novel multi-modal fusion network for robust human segmentation in low-light conditions. The proposed Swin-MFA network significantly improves accuracy and performance in challenging, dimly lit environments.

Keywords:

depth-sensing low light environment multi-modal fusion network segmentation

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Area of Science:

Computer Vision
Deep Learning
Image Segmentation

Background:

Deep learning-based image segmentation is crucial for security and monitoring.
Traditional algorithms struggle with low-illumination conditions, hindering human detection.
Nighttime monitoring frequently involves scenes with minimal or no light.

Purpose of the Study:

To develop a robust multi-modal fusion network for human segmentation in low-light environments.
To address the limitations of existing image segmentation methods under poor illumination.
To introduce a novel dataset for low-light human segmentation.

Main Methods:

Proposed a multi-modal fusion network with an encoder-decoder structure utilizing Swin-Transformer backbones.
Integrated RGB and depth features using a multi-scale fusion attention block.
Introduced the Low Light-Human Segmentation (LLHS) dataset with aligned RGB and depth images under low illuminance.

Main Results:

The Swin-MFA network achieved a mean Intersection over Union (mIoU) of 81.0 on the LLHS dataset.
Demonstrated superior performance compared to existing methods like ACNet, 3DGNN, ESANet, RedNet, and RFNet.
Showcased significant robustness and accuracy in varying low-light illumination levels.

Conclusions:

The proposed Swin-MFA network offers a significant advancement in human segmentation for low-light security and monitoring applications.
The LLHS dataset provides a valuable resource for further research in low-light image analysis.
Multi-modal fusion with Swin-Transformers proves effective in overcoming illumination challenges in image segmentation.