Fusulinid Detection Based on Deep Learning Single-Stage Algorithm

被引：1

作者：

Xi Y. ^{[1
]}

Wang Y. ^{[1
]}

Lu B. ^{[1
]}

Xing Z. ^{[2
]}

Hou G. ^{[2
]}

机构：

[1] Henan Polytechnic University, College of Computer Science and Technology, Jiaozuo

[2] Henan Polytechnic University, College of Resources and Environment, Jiaozuo

来源：

Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2024年 / 49卷 / 03期

关键词：

Carboniferous-Permian; deep learning; fusulinid; knowledge distillation; object detection; stratigraphy;

D O I：

10.3799/dqkx.2022.427

中图分类号：

学科分类号：

摘要：

Fusulinids are important standard fossils of the Carboniferous and Permian periods. The identification of fusulinids is significant for determining the geological age and delineating the Carboniferous-Permian stratigraphy. Considering the limitations of current fossil detection methods, a fusulinid detection method based on a deep learning single-stage algorithm is proposed. Taking fusulinids as the research object, the original model is improved by channel pruning by jointly optimizing the weight loss function and L1 regularization of the BN layer scale factor to compress the model size. Furthermore, the knowledge distillation is utilized to restore the detection performance of the pruned model. The experimental results show that the method can achieve the classification and localization of the fusulinids in the thin section images. The average accuracy reaches 98.1%, which meets the requirements of the real-time detection model. In addition, the number of model parameters is reduced by 74.1%, which solves the problems such as the lack of arithmetic power existing in real scenes. The method can effectively achieve the detection of fusulinids, while extending the applicability of the model to embedded devices and providing more possibilities for deep learning to perform intelligent recognition in paleontological fossil images. © 2024 China University of Geosciences. All rights reserved.

引用

页码：1154 / 1164

页数：10

共 29 条

[1]

Bochkovskiy A., Wang C. Y., Liao H. Y. M., YOLOv4: Optimal Speed and Accuracy of Object Detection, (2020)

[2]

Bu J. J., He W. H., Zhang K. X., Et al., Evolution of the Paleo-Asian Ocean: Evidences from Paleontology and Stratigraphy, Earth Science, 45, 3, pp. 711-727, (2020)

[3]

Denil M., Shakibi B., Dinh L., Et al., Predicting Parameters in Deep Learning, (2013)

[4]

Du Y. S., Tong J. N., Introduction to Palaeontology and Historical Geology, (2009)

[5]

Girshick R., Fast R - CNN, 2015 IEEE International Conference on Computer Vision (ICCV), (2015)

[6]

Girshick R., Donahue J., Darrell T., Et al., Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, (2014)

[7]

Guo W., Lin Y. T., Liu G. H., Early Permian Ru-gose Coral Assemblage and Its Geological Significances in Xiwuqi of Inner Mongolia, Journal of Jilin University (Earth Science Edition), 33, 4, pp. 399-405, (2003)

[8]

He K. M., Zhang X. Y., Ren S. Q., Et al., Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016)

[9]

Hinton G., Vinyals O., Dean J., Distilling the Knowledge in a Neural Network, (2015)

[10]

Hou X. H., Feng L., Zheng M. P., Et al., Recognition Method of Potassium-Rich Lithium Brine Reservoir in Nanyishan, Earth Science, 47, 1, pp. 45-55, (2022)

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