Summary of Xai-enhanced Semantic Segmentation Models For Visual Quality Inspection, by Tobias Clement et al.
XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
by Tobias Clement, Truong Thanh Hung Nguyen, Mohamed Abdelaal, Hung Cao
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers develop a framework to improve the accuracy and transparency of visual quality inspection systems used in industries like manufacturing and logistics. The system combines computer vision and machine learning techniques with explainable AI (XAI) methods to provide detailed explanations for defect detection. The approach involves training models, generating XAI-based explanations, evaluating the effectiveness of these explanations, and augmenting model annotations based on expert insights. Experimental results show that XAI-enhanced models outperform original DeepLabv3-ResNet101 models in complex object segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a system that can quickly spot defects on products and explain why it made certain decisions. This would be incredibly useful in industries like manufacturing and logistics. A team of researchers has developed a framework to make this happen. They use a combination of computer vision, machine learning, and explanations to improve the accuracy and transparency of visual quality inspection systems. The system can train models, provide detailed explanations for defect detection, and even help improve model performance based on expert insights. |
Keywords
» Artificial intelligence » Machine learning
