Wood texture detection based on optimized deep belief network model with multiple feature fusion

Xiaolin Zhou; Ying Liu; Sining Pan; Shijun Luo

doi:10.1117/12.3026660

1 April 2024 Wood texture detection based on optimized deep belief network model with multiple feature fusion

Xiaolin Zhou, Ying Liu, Sining Pan, Shijun Luo

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 130823H (2024) https://doi.org/10.1117/12.3026660
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

Texture, as an important component of wood quality classification, is difficult to extract and distinguish due to its complex features. Based on the the traditional gray level co-occurrence matrix (GLCM), this paper introduces the local binary pattern (LBP) operator to extract the uniform rotation invariance characteristics of features for multi-feature fusion, resulting in more expressive texture feature expression. For the deep belief network (DBN) training algorithm, which may have problems such as low computational efficiency, slow convergence rate, and "dead zone", Leaky ReLU is introduced as an activation function and adaptive learning rate to optimize the DBN network model. The experimental results show that the proposed method has better recognition speed and accuracy compared to BP, ELM, SVM, CNN, etc.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaolin Zhou, Ying Liu, Sining Pan, and Shijun Luo "Wood texture detection based on optimized deep belief network model with multiple feature fusion", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 130823H (1 April 2024); https://doi.org/10.1117/12.3026660

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KEYWORDS

Education and training

Feature fusion

Cooccurrence matrices

Feature extraction

Detection and tracking algorithms

Mathematical optimization

Image fusion

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