| 摘要: |
| 河道山地灾害如泥石流、滑坡、山洪、水土流失等严重危害着河道周边公路、铁路、桥梁、大型水利工程等重要基础设施的安全。快速识别已发生的河道山地灾害意义重大，而传统巡检方式具有极高的危险性和滞后性，迫切需要新方法来替代。以深度卷积神经网络为代表的深度学习技术具有局部感知、参数共享、池化等多个特性，相比传统机器学习方法具有更强大的特征学习和特征表达能力。在深度学习开源框架下，利用大量河道山地灾害图片数据完成了Caffenet等多个深度模型的训练，并结合迁移学习方法，使河道山地灾害识别准确率最终达到90%以上，为河道山地灾害快速识别、群防群测体系的完善提供了新思路。 |
| 关键词: 河道山地灾害 卷积神经网络 图像识别 川藏公路 |
| DOI：10.12170/201902009 |
| 分类号:TU43 |
| 基金项目:国家重点研发计划资助项目(2016YFC0401600，2017YFC0404906)；国家自然科学基金资助项目（51779035，51769033）；中央高校基本科研业务费项目（DUT17ZD205） |
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| Fast recognition method for mountain hazards in river courses based on convolutional neural networks |
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ZHAO Penghui1, LI Junjie1,2, KANG Fei1
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1.School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian;2.Institute of Technology, Tibet University, Lhasa
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| Abstract: |
| The mountain hazards in the river courses such as the debris flow, landslide, mountain flood and soil erosion seriously threaten the safety of the important infrastructures such as roads, railways, bridges and large-scale hydroprojects around the river. More than 90% of the traffic interruption in the Sichuan-Tibet highway are caused by the mountain hazards, which restrict the development of economy and society in Tibet. It is of great significance to quickly identify the mountain hazards in the river courses for timely adoption of appropriate emergency disaster plans and release evacuation information. The observation of the mountain hazards in the river courses by professional inspectors in a traditional way is of great danger and obvious lag. Therefore, it is urgent to study a new method as an alternative. With the arrival of the big data era, a deep learning technology represented by the convolution neural network has many characteristics such as local connectivity, parameter sharing and pooling, and has more powerful features and learning skills compared with the traditional machine learning method. This study has completed the training of Caffenet and other depth models by using a large number of image data of the mountain hazards in the river courses with the deep learning open source framework. By use of the transfer learning, the recognition accuracy eventually reached more than 90 percent. The analysis results show that the recognition method provides a new idea for the fast recognition of the typical mountain hazards in the river courses and the improvement of the group measuring system. |
| Key words: mountain hazards in river courses convolutional neural networks image recognition Sichuan Tibet highway |
