PDF(4679 KB)
Fusulinid Detection Based on Deep Learning Single-Stage Algorithm
Xi Yuanyuan, Wang Yongmao, Lu Bibo, Xing Zhifeng, Hou Guangshun
PDF(4679 KB)
PDF(4679 KB)
Fusulinid Detection Based on Deep Learning Single-Stage Algorithm
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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 / Carboniferous-Permian / knowledge distillation / stratigraphy
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感谢匿名审稿专家提出的有益建议！
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