基于深度学习单阶段算法的䗴类化石检测

奚园园; 王永茂; 芦碧波; 邢智峰; 侯广顺

doi:10.3799/dqkx.2022.427

Volume 49 Issue 3

Mar. 2024

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Article Navigation > Earth Science > 2024 > 49(3): 1154-1164

Xi Yuanyuan, Wang Yongmao, Lu Bibo, Xing Zhifeng, Hou Guangshun, 2024. Fusulinid Detection Based on Deep Learning Single-Stage Algorithm. Earth Science, 49(3): 1154-1164. doi: 10.3799/dqkx.2022.427

Citation:

Xi Yuanyuan, Wang Yongmao, Lu Bibo, Xing Zhifeng, Hou Guangshun, 2024. Fusulinid Detection Based on Deep Learning Single-Stage Algorithm. Earth Science, 49(3): 1154-1164. doi: 10.3799/dqkx.2022.427

Xi Yuanyuan, Wang Yongmao, Lu Bibo, Xing Zhifeng, Hou Guangshun, 2024. Fusulinid Detection Based on Deep Learning Single-Stage Algorithm. Earth Science, 49(3): 1154-1164. doi: 10.3799/dqkx.2022.427

Citation:

Xi Yuanyuan, Wang Yongmao, Lu Bibo, Xing Zhifeng, Hou Guangshun, 2024. Fusulinid Detection Based on Deep Learning Single-Stage Algorithm. Earth Science, 49(3): 1154-1164. doi: 10.3799/dqkx.2022.427

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Fusulinid Detection Based on Deep Learning Single-Stage Algorithm

doi: 10.3799/dqkx.2022.427

1.
Henan Polytechnic University, College of Computer Science and Technology, Jiaozuo 454003, China
2.
Henan Polytechnic University, College of Resources and Environment, Jiaozuo 454003, China

Received Date: 2022-06-13
Available Online: 2024-04-12
Publish Date: 2024-03-25

Abstract

Abstract

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.
- fusulinid,
- deep learning,
- object detection,
- CarboniferousPermian,
- knowledge distillation,
- stratigraphy

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References(42)

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Fusulinid Detection Based on Deep Learning Single-Stage Algorithm

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