From Pixels to Insight: Enhancing Metallic Component Defect Detection with GLCM Features and AI Explainability
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3242-5_20
Reference15 articles.
1. Nath V, Chattopadhyay C, Desai KA (2022) On enhancing prediction abilities of vision-based metallic surface defect classification through adversarial training. Eng Appl Artificial Intell
2. Luo Q, Fang X, Liu L, Yang C, Sun Y (2020) Automated visual defect detection for flat steel surface: a survey. IEEE Trans Instrum Meas 69(3):626–644
3. Nath V, Chattopadhyay C, Desai KA (2022) NSLNet: an improved deep learning model for steel surface defect classification utilizing small training datasets. Manufact Lett 35:39–42
4. Cheng X, Yu J (2020) RetinaNet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection. IEEE Trans Instrum Meas 70:1–11
5. Zhao W, Chen F, Huang H, Li D, Cheng W (2021) A new steel defect detection algorithm based on deep learning. Comput Intell Neurosci 2021:1–13
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