| 王仁超,连嘉欣,邸阔.结合深度学习和NCFS算法的堆石料粒度分布智能检测方法[J].水利学报,2021,52(9):1103-1115 |
| 结合深度学习和NCFS算法的堆石料粒度分布智能检测方法 |
| Intelligent detection method of rockfill particle size distribution based on Deep-learning and NCFS algorithm |
| 投稿时间：2020-10-04 |
| DOI：10.13243/j.cnki.slxb.20200806 |
| 中文关键词: 堆石坝粒度检测 深度学习 Deeplabv3+模型 稠密条件随机场 NCFS算法 |
| 英文关键词: size detection of rockfill dams deep-learning Deeplabv3+ DenseCRF NCFS algorithm |
| 基金项目: |
|
| 摘要点击次数: 713 |
| 全文下载次数: 482 |
| 中文摘要: |
| 针对目前堆石坝施工过程中人工筛分试验无法实现爆堆料物粒度快速检测以及现有粒度检测模型准确度低、泛化能力差等问题，提出了一种基于深度学习模型与邻域分量特征（Neighborhood Component Feature Selection，NCFS）算法相结合的堆石坝料物粒度数字筛分检测方法，该方法可以通过拍摄料堆图像快速检测料堆粒度分布。为了提高深度学习模型的精确度，提出将基于迁移学习的Deeplabv3+模型和稠密条件随机场算法（DenseCRF）结合用于图像训练学习和优化；在料堆二维特征到三维粒度分布转换方面，提出基于NCFS算法的块石二维平面参数对三维粒度的表征公式，并采用MATLAB语言编制了相应的软件加以实现。句容抽水蓄能电站工程现场爆破料堆图像采集和筛分试验分析的结果表明：所提方法是可行的，且相比其他方法，在特征提取以及粒度检测精度上均有所提高。 |
| 英文摘要: |
| Focus on the problems such as the failure of rapid detection of blasting material particle size by artificial screening,and the low accuracy and poor generalization of the existing particle size detection model in the construction process of rockfill dam, a method which combined Deep-learning and NCFS algorithm to detect the particle size of rockfill dams in digital screening is presented, the method could detect the particle size through the images of blasting pile quickly. Aiming at improving the accuracy of training result of Deep-learning model, the DenseCRF algorithm is utilized to optimize the result of image training by Deeplabv3+ model. In order to transform the 2D features extracted from image to 3D particle size distribution of pile, the NCFS algorithm is exploited to characterize the relationship between 2D parameters and 3D particle size, and a software to realize the transformation is programmed by MATLAB. Through images acquisition and screening test analysis of blasting piles in Jurong pumped storage power station, the result shows that,compared with other methods,the proposed method is feasible,and the accuracy of feature extraction and particle size prediction is improved. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |