Summary of Defect Detection in Tire X-ray Images: Conventional Methods Meet Deep Structures, by Andrei Cozma et al.
Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures
by Andrei Cozma, Landon Harris, Hairong Qi, Ping Ji, Wenpeng Guo, Song Yuan
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 The paper proposes an effective approach for detecting defects in tire X-ray images using a combination of traditional feature extraction methods, such as Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) features, Fourier and Wavelet-based features, and advanced machine learning techniques. The study highlights the importance of feature engineering to enhance the performance of defect detection systems. By integrating combinations of these features with a Random Forest (RF) classifier and comparing them against models like YOLOv8, the research benchmarks the performance of traditional features in defect detection and explores their synergy with modern approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to find defects in tire X-ray pictures by combining old and new ways of looking at data. They use techniques like Local Binary Pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) to find patterns, as well as newer methods like Fourier and Wavelet-based features. Then, they combine these patterns with a special kind of machine learning called Random Forest (RF). The researchers compare their method to others, like YOLOv8, to see how well it works. |
Keywords
* Artificial intelligence * Feature engineering * Feature extraction * Machine learning * Random forest
