| 基于双重注意力机制的滑坡识别方法优化 |
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| 引用本文： | 吴琪,周创兵,黄发明,姚池.基于双重注意力机制的滑坡识别方法优化[J].地质科技通报,2022,41(2):246-253. |
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| 作者姓名： | 吴琪 周创兵 黄发明 姚池 |
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| 基金项目： | 中国中铁股份公司重点课题2019-重点-42-01 |
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| 摘 要： | 随着计算机视觉技术的发展, 通过卫星图像深度学习进行滑坡识别的研究正在逐步展开。通过引入双重注意力机制, 提出了一种基于卷积神经网络的滑坡图像识别优化算法。基于统计的2 200张滑坡图像数据集, 探讨了10种网络结构及4种注意力机制对滑坡识别结果的影响, 并通过比例为4∶1的训练集和测试集进行滑坡识别, 验证了本文方法的有效性。结果表明: ResNet结构相较于其他网络结构表现更为优秀, 就该算例而言, ResNet-101结构具有最高的召回率、精确率和F1度量。融入了双重注意力机制的卷积神经网络相较于单个神经网络而言, 滑坡识别的精确率更大, 且滑坡边界分割结果更接近于真实的滑坡边界, 其中, ResNet-101+DAN模型为最优模型。相较之下, 单个神经网络无法克服图像噪声的影响, 图像分割结果不佳。
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| 关 键 词： | 滑坡检测 卷积神经网络 自动识别 注意力机制 滑坡数据集 方法优化 |
| 收稿时间： | 2021-11-11 |
Optimization of the landslide identification method based on a dual attention mechanism |
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| Abstract: | With the development of computer vision technology, studies on landslide identification have gradually been carried out by means of deep learning. By introducing the dual attention model, an optimization algorithm for landslide image recognition based on a convolutional neural network is proposed in this paper. Based on 2 200 landslide image datasets, this paper discusses the effects of 10 network structures and 4 attention models on landslide recognition results. The effectiveness of this method is verified by using a 4∶1 training set and test set for landslide recognition. The results show that the ResNet structure performs better than other network structures. For this example, the ResNet-101 structure has the highest recall rate, precision rate and F1-measure. Compared with a single neural network, the convolutional neural network with a dual attention model has a higher accuracy of landslide identification, and the segmentation result of the landslide boundary is closer to the real landslide boundary. Among them, the ResNet-101+DAN model is the optimal model. In contrast, a single neural network cannot overcome the influence of the image noise, and the result of the image segmentation is poor. |
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