Summary of Mscrackmamba: Leveraging Vision Mamba For Crack Detection in Fused Multispectral Imagery, by Qinfeng Zhu et al.
MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagery
by Qinfeng Zhu, Yuan Fang, Lei Fan
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel two-stage paradigm called MSCrackMamba is proposed for vision-based crack detection in structural health monitoring. The approach leverages Vision Mamba and a super-resolution network to address challenges such as aligning infrared (IR) and RGB channels, limited receptive fields, and high computational complexity. By applying super-resolution to IR channels to match the resolution of RGB channels, MSCrackMamba enables data fusion and improves crack detection performance. Experimental results on the large-scale Crack Detection dataset Crack900 demonstrate an improvement of 3.55% in mean intersection over union (mIoU) compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Crack detection is important for keeping bridges, buildings, and roads safe from damage. This study proposes a new way to do this using computer vision and artificial intelligence. They developed a special kind of neural network that can combine different types of images together to find cracks more accurately. The new approach is tested on a large dataset and shows improved results compared to other methods. |
Keywords
» Artificial intelligence » Neural network » Super resolution
