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摘要： 我国尾矿库事故频发，所造成的危害极其严重。掌握尾矿库的数量及分布情况对预防尾矿库事故和开展尾矿库应急工作具有重大意义。传统的调查方法主要以地面调查为主，难以做到大范围高频次的监测。因此提出了一种基于深度学习的尾矿库目标检测方法，可以快速识别尾矿库的位置并掌握其地理分布。首先分析尾矿库在遥感图像上的特征，制作适合训练的样本，根据样本的情况优化调整训SSD （Single Shot Multibox Detector）模型，基于优化后的模型进行京津冀地区尾矿库的自动提取。实验结果表明：京津冀地区检测出尾矿库2 696座，召回率达到93.3%。说明采用深度学习目标检测的方法提取尾矿库，取得了较好的效果，所提出的尾矿库提取方法可应用于全国及全球尾矿库的提取。 关键词： 遥感;  深度学习;  目标检测;  尾矿库;  京津冀     Abstract: The accidents of the tailing ponds in China are frequent， the damage caused by dam breaking is extremely serious. The current quantity and distribution of tailings pond is necessary for preventing tailings pond accidents and carrying out emergency work in tailings pond. The traditional survey method is mainly based on ground investigations， which is difficult to achieve large-scale high-frequency monitoring. A tailing pond detection method based on deep learning detection was proposed in this paper， which can quickly identify the locations of the tailing ponds and obtain their geographical distribution. The suitable training samples are produced based on the study of the characteristics of the tailing ponds on the remote sensing image. SSD （Single Shot Multibox Detector） model is adjusted according to the samples characteristics during the model training. The extraction of the tailing ponds in the Beijing-Tianjin-Hebei Region is realized based on optimized model. The experiment result shows that there are 2 696 tailing ponds which were detected in the Beijing-Tianjin-Hebei Region，the recall reaches 93.3%.This paper realized the extract the tailings pond with the method of deep learning target detection， and has achieve good results which can provides method for the national and global extraction of tailing ponds.

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