Summary of Ocr Is All You Need: Importing Multi-modality Into Image-based Defect Detection System, by Chih-chung Hsu and Chia-ming Lee and Chun-hung Sun and Kuang-ming Wu
OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System
by Chih-Chung Hsu, Chia-Ming Lee, Chun-Hung Sun, Kuang-Ming Wu
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents an innovative approach to automatic optical inspection (AOI) for industrial manufacturing and quality control. AOI typically relies on high-resolution imaging instruments, analyzing image textures or patterns to detect anomalies. However, traditional AOI faces challenges such as limited sample sizes, variations in source domains, and sensitivities to changes in lighting and camera positions. To address these issues, the authors introduce an external modality-guided data mining framework, leveraging optical character recognition (OCR) to extract statistical features from images. The OANet (Ocr-Aoi-Net) model aligns external modality features with image features encoded by a convolutional neural network, enhancing semantic representations and fusion capabilities. Experimental results demonstrate significant boosts in the recall rate of defect detection models while maintaining robustness in challenging scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines better at inspecting things they make. Machines use cameras to look for problems with what they’re making. But sometimes, it’s hard for them to get it right. This happens because there aren’t many examples of what they should be looking for, and the lighting or camera angle might be different each time. To fix this, the researchers created a new way to use computer vision and machine learning together. They combined images with information from other sources to help the machines make better decisions. The results show that their method is much more accurate and reliable than what was being used before. |
Keywords
* Artificial intelligence * Machine learning * Neural network * Recall
