Novel Rock Image Classification: The Proposal and Implementation of HKUDES_Net

Zhou, Yimeng; Wong, Louis Ngai Yuen; Tse, Keith Ki Chun

doi:10.1007/s00603-023-03235-0

Cited by 9 publications

(1 citation statement)

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“…Subsequently, the model learns the essential features of the images in this manner, providing crucial discriminative information for the micro-model through model distillation. Zhou and colleagues [16] proposed and implemented a nextgeneration convolutional neural network (CNN) named HKUDES_Net, designed to classify seven rock types with similar textures and colors. Leveraging computational strategies such as dynamic dilation and squeeze-and-excitation, HKUDES_Net can effectively classify rock types with varying granularity.…”

Section: Related Workmentioning

confidence: 99%

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Deng,

Han,

Liu

et al. 2024

Electronics

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The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.

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Section: Related Workmentioning

confidence: 99%