WoodGLNet: a multi-scale network integrating global and local information for real-time classification of wood images-Reference-Cited by-同舟云学术

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WoodGLNet: a multi-scale network integrating global and local information for real-time classification of wood images

Published:2024-08 Issue:4 Volume:21 Page:
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Zheng Zhishuai,Ge Zhedong,Tian Zhikang,Yang Xiaoxia,Zhou Yucheng

Funder

The Natural Science Foundation of Shandong Province, China

College Students' Innovative Entrepreneurial Training Plan Program

The Taishan Scholar Advantage Characteristic Discipline Talent Team Project of Shandong Province of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11554-024-01521-w.pdf

Reference51 articles.

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2. He, K., Zhang, X., Sun, S., et al. Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

3. Atrey, K., Singh, B.K., Bodhey, N.K.: Integration of ultrasound and mammogram for multimodal classification of breast cancer using hybrid residual neural network and machine learning. Image Vis. Comput. 145, 1–9 (2024)

4. Han, X., Li, T., Bai, C., et al.: Integrating prior knowledge into a bibranch pyramid network for medical image segmentation. Image Vis. Comput. 143, 1–15 (2024)

5. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: CVPR, pp. 7464–7475 (2023)

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