Defect Detection in Metal Surfaces Using Computer Vision
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9442-7_4
Reference13 articles.
1. Nath V, Chattopadhyay C, Desai KA (2023) On enhancing prediction abilities of vision-based metallic surface defect classification through adversarial training. Eng Appl Artif Intell 117:105553 (Elsevier BV). https://doi.org/10.1016/j.engappai.2022.105553
2. da Costa AZ, Figueroa HEH, Fracarolli JA (2020) Computer vision-based detection of external defects on tomatoes using deep learning. Biosyst Eng 190:131–144 (Elsevier BV). https://doi.org/10.1016/j.biosystemseng.2019.12.003
3. Rasheed A et al (2020) Fabric defect detection using computer vision techniques: a comprehensive review. Math Probl Eng 2020:1–24 (Hindawi Limited). https://doi.org/10.1155/2020/8189403
4. Koch C, Georgieva K, Kasireddy V, Akinci B, Fieguth P (2015) A review on computer vision-based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv Eng Inf 29(2):196–210 (Elsevier BV). https://doi.org/10.1016/j.aei.2015.01.008
5. Risheh A, Tavakolian P, Melinkov A, Mandelis A (2022) Infrared computer vision in non-destructive imaging: sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering. In: NDT & E International, vol 125. Elsevier BV, p 102568. https://doi.org/10.1016/j.ndteint.2021.102568
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