PDF(4679 KB)
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
PDF(4679 KB)
PDF(4679 KB)
基于深度学习单阶段算法的䗴类化石检测
Fusulinid Detection Based on Deep Learning Single-Stage Algorithm
,
䗴化石是石炭纪、二叠纪重要的标准化石，其详细的鉴定工作对确定地质时代和划分石炭系‒二叠系具有重要意义.鉴于目前䗴类化石检测方法的局限性，提出一种基于深度学习单阶段算法的䗴类化石检测.以䗴类化石为研究对象，对原始模型进行分析，之后联合优化权重损失函数和BN层尺度因子的L1正则化等方式进行通道剪枝，再使用知识蒸馏使剪枝后模型恢复检测性能.实验结果表明，该方法可实现薄片图像中䗴类所在区域的定位和分类，平均精度均值达到98.1%，满足实时检测模型的要求，并且剪枝后参数量压缩了74.1%，解决了真实场景中存在的算力缺乏等问题.该方法能够有效保证䗴类化石的检测效果，同时扩展了该模型在嵌入式设备的适用范围，为深度学习在古生物化石图像的智能识别方面提供更多可能性.
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.
䗴类化石 / 深度学习 / 目标检测 / 石炭系‒二叠系 / 知识蒸馏 / 地层学
fusulinid / deep learning / object detection / Carboniferous-Permian / knowledge distillation / stratigraphy
P534.6
|
Bochkovskiy, A., Wang, C. Y., Liao, H. Y. M., 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv, 2004.10934. https://arxiv.org/abs/2004.10934
|
|
Bu, J. J., He, W. H., Zhang, K. X., et al., 2020. Evolution of the Paleo-Asian Ocean: Evidences from Paleontology and Stratigraphy. Earth Science, 45(3): 711-727 (in Chinese with English abstract).
|
|
Denil, M., Shakibi, B., Dinh, L., et al., 2013. Predicting Parameters in Deep Learning. ArXiv, 1306.0543. https://doi.org/10.48550/arXiv.1306.0543
|
|
Du, Y. S., Tong, J. N., 2009. Introduction to Palaeontology and Historical Geology. China University of Geosciences Press, Wuhan (in Chinese).
|
|
Girshick, R., 2015. Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV). Santiago. https://doi.org/10.1109/ICCV.2015.169
|
|
Girshick, R., Donahue, J., Darrell, T., et al., 2014. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus. https://doi.org/10.1109/CVPR.2014.81
|
|
Guo, W., Lin, Y. T., Liu, G. H., 2003. Early Permian Rugose Coral Assemblage and Its Geological Significances in Xiwuqi of Inner Mongolia. Journal of Jilin University (Earth Science Edition), 33(4): 399-405 (in Chinese with English abstract).
|
|
He, K. M., Zhang, X. Y., Ren, S. Q., et al., 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas. https://doi.org/10.1109/CVPR.2016.90
|
|
Hinton, G., Vinyals, O., Dean, J., 2015. Distilling the Knowledge in a Neural Network. ArXiv, 1503.02531. https://doi.org/10.48550/arXiv.1503.02531
|
|
Hou, X. H., Feng, L., Zheng, M. P., et al., 2022. Recognition Method of Potassium-Rich Lithium Brine Reservoir in Nanyishan. Earth Science, 47(1): 45-55 (in Chinese with English abstract).
|
|
Huang, Z., Bai, Z. Q., Chai, H., 2009.Identification of the ‘Ambiguity Features’ of the Conodont by the Artificial Neural Network. Geological Science and Technology Information, 28(3): 94-98 (in Chinese with English abstract).
|
|
Li, P. F., Wang, H., Wu, Y. C., et al., 2022. Recognition of Dangerous Rock Mass and Seismic Risk Analysis of Highway Based on UAV Images. China Earthquake Engineering Journal, 44(4): 777-785 (in Chinese with English abstract).
|
|
Liu, W., Anguelov, D., Erhan, D., et al., 2016. Ssd: Single Shot MultiBox Detector. 2016 European Conference on Computer Vision, Amsterdam. https://doi.org/10.1007/978-3-319-46448-0_2
|
|
Liu, X. Y., 2018. The Application of Image Recognition Technology in Paleontological Fossil Image (Dissertation). Jilin University, Changchun (in Chinese with English abstract).
|
|
Liu, Z., Li, J. G., Shen, Z. Q., et al., 2017. Learning Efficient Convolutional Networks through Network Slimming. 2017 IEEE International Conference on Computer Vision (ICCV). Venice. https://doi.org/10.1109/ICCV.2017.298
|
|
Lowe, D. G., 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2): 91-110. https://doi.org/10.1023/B: VISI.0000029664.99615.94
|
|
Mehta, R., Ozturk, C., 2018. Object Detection at 200 Frames Per Second. ArXiv, 1805.06361. https://doi.org/10.48550/arXiv.1805.06361
|
|
Peng, X. D., Zhang, M. S., Li, X. M., 1999. The Evolution of Paleozoic Tectonic Paleogeography in Jilin and Heilongjiang Orogenic Belt. World Geology, 18(3): 24-28 (in Chinese).
|
|
Redmon, J., Divvala, S., Girshick, R., et al., 2016. You only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas. https://doi.org/10.1109/CVPR.2016.91
|
|
Redmon, J., Farhadi, A., 2018. Yolov3: An Incremental Improvement. arXiv, 1804.02767. https://doi.org/10.48550/arXiv.1804.02767
|
|
Ren, S. Q., He, K. M., Girshick, R., et al., 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv, 1506.01497. https://doi.org/10.48550/arXiv.1506.01497
|
|
Shen, S. Z., Zhang, H., Zhang, Y. C., et al., 2019. Permian Integrative Stratigraphy and Timescale of China. Science in China (Series D), 49(1): 160-193 (in Chinese).
|
|
Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Visual Recognition. ArXiv, 1409.1556. https://doi.org/10.48550/arXiv.1409.1556
|
|
Szegedy, C., Liu, W., Jia, Y. Q., et al., 2015. Going Deeper with Convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., Boston. https://doi.org/10.1109/CVPR.2015.7298594
|
|
Weller, A. F., Harris, A. J., Ware, J. A., 2006. Artificial Neural Networks as Potential Classification Tools for Dinoflagellate Cyst Images: A Case Using the Self- Organizing Map Clustering Algorithm. Review of Palaeobotany and Palynology, 141(3-4): 287-302. https://doi.org/10.1016/j.revpalbo.2006.06.001
|
|
Yu, X. L., Ye, K., Du, C. J., et al., 2021. Microscopic Recognition of Micro Fossils in Carbonate Rocks Based on Convolutional Neural Network. Petroleum Geology & Experiment, 43(5): 880-885, 895 (in Chinese with English abstract).
|
|
Yue, X., Hu, H., Jia, J. Z., 2019. A Method for Identifying Foraminifera Fossils Based on Deep Learning. Computer Knowledge and Technology, 15(27): 173, 178 (in Chinese).
|
|
Zhang, Z. C., 1987. Correlation of the Shanxi Stage in North China and Related Fusulinid-Bearing Strata in Certain Areas. Regional Geology of China, 6(4): 359-363 (in Chinese).
|
|
Zuo, R. G., Peng, Y., Li, T., et al., 2021. Challenges of Geological Prospecting Big Data Mining and Integration Using Deep Learning Algorithms. Earth Science, 46(1): 350-358 (in Chinese with English abstract).
|
感谢匿名审稿专家提出的有益建议！
/
| 〈 |
|
〉 |
AI Summary
