Summary of Crackess: a Self-prompting Crack Segmentation System For Edge Devices, by Yingchu Wang et al.
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices
by Yingchu Wang, Ji He, Shijie Yu
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 computer vision-based system for detecting and segmenting concrete cracks in infrastructure is introduced. The system, called CrackESS, leverages YOLOv8 and self-prompted SAM models with fine-tuning using LoRA, followed by a proposed refinement module to improve segmentation accuracy. The approach demonstrates excellent performance on three datasets (Khanhha’s dataset, Crack500, and CrackCR) and is validated through experiments on a climbing robot system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Detecting cracks in infrastructure can be crucial for maintenance and sustainability. Researchers have developed computer vision methods to help automate this process. However, existing approaches face challenges in efficiency and accuracy. This new system, called CrackESS, combines two models (YOLOv8 and SAM) with a refinement module to detect and segment concrete cracks accurately. The system was tested on three datasets and worked well with a climbing robot. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Sam
