Detection of natural wood defects with large color differences based on branched network-Reference-Cited by-同舟云学术

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Detection of natural wood defects with large color differences based on branched network

Published:2023-05-02 Issue:29 Volume:82 Page:44719-44739
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Wang Xiao

Funder

the special fund of basic research funding of Chinese central nonprofit research institutions

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15487-7.pdf

Reference30 articles.

1. Bhargava A, Bansal A (2020) Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging. Multimed Tools Appl 79:7857–7874

2. Chacon MI, Alonso GR (2006) Wood defects classification using a SOM/FFP approach with minimum dimension feature vector. In: Advances in neural networks. Springer, pp 1105–1110. https://doi.org/10.1007/11760191_161

3. Ding F, Zhuang Z, Liu Y, Jiang D, Yan X, Wang Z (2020) Detecting defects on solid wood panels based on an improved SSD algorithm. Sensors (Basel) 20(18):5315

4. Du X, Li J, Feng H, Chen S (2018) Image reconstruction of internal defects in wood based on segmented propagation rays of stress waves. Appl Sci 8(10):1778

5. Fan J, Liu Y, Yang Y, Gou B (2020) Research progress in the application of machine vision to wood defect detection. World For Res 33(3):32–37

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1. SiM-YOLO: A Wood Surface Defect Detection Method Based on the Improved YOLOv8;Coatings;2024-08-07

2. Identification and localization of veneer knot defects based on parallel structure fusion approach;European Journal of Wood and Wood Products;2024-05-04

3. Pinewood Knot Detection Method Using Color Analysis;2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2023-06-29

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