Research on rock image classification algorithm based on multi-task learning and pseudo-labels

Zijian Huang; Yanxia Li; Qingqing Tian; Xiaogang Zhang; Lihua Tian

doi:10.1117/12.3009444

16 October 2023 Research on rock image classification algorithm based on multi-task learning and pseudo-labels

Zijian Huang, Yanxia Li, Qingqing Tian, Xiaogang Zhang, Lihua Tian

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030B (2023) https://doi.org/10.1117/12.3009444
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

With the continuous increase of drilling speed in rock debris logging, the corresponding rock identification speed is required to be constantly improved. Therefore, this paper proposes a rock identification algorithm based on rock debris images, which is based on multi-task learning and pseudo-labels. The algorithm first uses affine transformation matrix for data augmentation and builds a multi-task neural network framework based on hard sharing mechanism, which uses rock debris color and texture as two sub-task modules for classification. Secondly, the algorithm uses unlabeled rock debris images as pseudo-labels for the original images and proposes a pseudo-label filtering method for multi-task learning, which adds pseudo-labeled images to the original dataset for model iteration to improve the accuracy and generalization ability of the model. The experiments show that this algorithm can meet the current requirements of rock identification speed and accuracy in rock debris logging.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zijian Huang, Yanxia Li, Qingqing Tian, Xiaogang Zhang, and Lihua Tian "Research on rock image classification algorithm based on multi-task learning and pseudo-labels", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030B (16 October 2023); https://doi.org/10.1117/12.3009444

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KEYWORDS

Image classification

Education and training

Machine learning

Data modeling

Feature extraction

Image processing

Performance modeling

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