A hybrid attention multi-scale fusion network for real-time semantic segmentation
2025
A hybrid attention multi-scale fusion network for real-time semantic segmentation
publication
Evidence: high
Author Information
Author(s): Ye Baofeng, Wu Qianlong
Primary Institution: Qiqihar University, China
Hypothesis
The study proposes a new method to improve semantic segmentation accuracy by addressing the loss of spatial information in existing algorithms.
Conclusion
The proposed method achieves a mean Intersection over Union (mIoU) of 73.6% at 176 frames per second on the Cityscapes dataset, demonstrating improved accuracy and speed.
Supporting Evidence
- The proposed method achieves 73.6% mIoU at 176 FPS on the Cityscapes dataset.
- Edge detection methods were incorporated to enhance boundary information extraction.
- The study addresses the loss of spatial information in existing semantic segmentation algorithms.
Takeaway
This study created a new way to help computers understand images better by keeping important details while still being fast.
Methodology
The study designed two new modules, HFRM and HFFM, to enhance feature extraction and fusion in semantic segmentation tasks, incorporating edge detection methods.
Digital Object Identifier (DOI)
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