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针对城市土地资源变化检测工作繁杂、工作量大、自动化程度低等问题，本文提出一种基于深度学习模型的高分辨率遥感影像建筑物变化检测方法，将语义分割的思想引入到遥感变化检测。基于残差结构特征较卷积层提取性能更优和特征金字塔网络多尺度预测的特点，将残差结构和特征金字塔网络融合到Unet模型中，建立FPN Res-Unet模型。该模型以Unet为基础，引入ResNet18的残差结构作为编码路径特征提取层，在每次卷积后使用边界填充，使得输入图像和输出图像尺寸一致；在解码路径每级上采样过程中，拓展支路径将特征金字塔网络融合到模型的网络主干中，将残差结构、Unet及特征金字塔网络的优点相互融合，增强了Unet的特征提取，弥补了语义分割网络对小目标检测的欠缺；在获取深层语义信息的同时关注细节信息，提高建筑物变化检测精度。实验表明，该方法在所用数据集，准确率、召回率、F1 3种指标均达到90%以上。 相似文献
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为避免密集人群踩踏事件发生,从监控图像中准确获取密集人群人数信息非常重要。针对密集人群计数难度大、人群目标小、场景尺度变化大等特点,本文提出一种新型神经网络结构VGG-ResNeXt。本网络使用VGG-16的前10层作粗粒度特征提取器,使用改进的残差神经网络作为细粒度特征提取器。利用改进的残差神经网络“多通道,共激活”的特点,使得单列式人群计数神经网络获得了多列式人群计数网络的优点（即从小目标、多尺度的密集人群图像中提取更多人群特征）,同时避免了多列式人群计数网络训练难度大、结构冗余等缺点。实验结果表明本模型在UCF-CC-50数据集、ShangHaiTech B数据集和UCF-QNRF数据集中取得了最高精度,MAE指标分别优于其他同期模型7.5%、18.8%和2.4%,证明了本模型的在计数精度方面的有效性。本研究成果可以有效帮助城市管理,有效缓解公安疏导压力,保障人民生命财产安全。 相似文献
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Yibin Ren Huanfa Chen Tao Cheng Yang Zhang Ge Chen 《International journal of geographical information science》2020,34(4):802-823
ABSTRACTThe spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns. 相似文献
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为实现对养殖鳗鲡(Anguilla)摄食强度的准确评估,提出了一种基于双流残差卷积神经网络的鳗鲡摄食强度评估方法,该方法针对传统双流网络(Two-stream)中存在的问题做出了相应的改进。首先针对传统双流网络存在网络结构较浅,无法提取到充分的鳗鲡摄食行为特征的问题,选择使用ResNet50网络进行替换,以提取到更具代表性的特征。其次针对传统双流网络最后的分类结果是把空间流和时间流的得分取平均值融合而获得,这种方式较为简单,且其空间流和时间流网络为独立进行训练,容易导致网络出现学习不到鳗鲡摄食行为的时空关联特征的问题,选择使用特征层融合方式对空间流和时间流网络提取获得的特征进行融合,让网络能够并行进行训练,以提取到时空信息间的关联特征。试验结果表明:文内提出的基于双流残差卷积神经网络的鳗鲡摄食强度评估方法准确率达到98.6%,与单通道的空间流和时间流网络相比,准确率分别提升了5.8%和8.5%,与传统的双流网络相比准确率也提升了3.2%。 相似文献
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随着下一代射电天文望远镜的不断改进和发展,脉冲星巡天观测将发现数百万个脉冲星候选体,这给脉冲星的识别和新脉冲星的发现带来了巨大挑战,迅速发展的人工智能技术可用于脉冲星识别.使用Parkes望远镜的脉冲星数据集(The High Time Resolution Universe Survey,HTRUS),设计了一个14层深的残差网络(Residual Network,ResNet)进行脉冲星候选体分类.在HTRUS数据样本中,存在非脉冲星候选体(负样本)的数目远远大于脉冲星候选体(正样本)数目的样本非均衡问题,容易产生模型误判.通过使用过采样技术对训练集中的正样本进行数据增强,并调整正负样本的比例,解决了正负样本非均衡问题.训练过程中,使用5折交叉验证来调节超参数,最终构建出模型.测试结果表明,该模型能够取得较高的精确度(Precision)和召回率(Recall),分别为98%和100%,F1分数(F1-score)能够达到99%,每个样本检测完成只需要7 ms,为未来脉冲星大数据分析提供了一个可行的办法. 相似文献
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《Chinese Astronomy and Astrophysics》2021,45(3):364-381
As the next-generation radio astronomical telescopes continuously improve and develop, the pulsar survey will produce millions of pulsar candidates, which pose considerable challenges for pulsar identification and classification. The rapidly evolving artificial intelligence (AI) techniques are being used for pulsar identification and discovery of new pulsars. Using the pulsar data set observed with the Parkes telescope, namely the High Time Resolution Universe Survey (HTRUS), a 14-layer deep residual network has been designed (called the Residual Network, ResNet) for pulsar candidate classifications. In the HTRUS sample data, the number of non-pulsar candidates (i.e., negative samples) is much larger than that of pulsar candidates (i.e., positive samples). The imbalance between the positive and negative samples is prone to result in model misjudgement. By using the over-sampling technique to enhance the data of positive samples in the training set and adjusting the ratio of positive and negative samples, we have solved this imbalance problem. In the training process, the hyperparameters are adjusted by means of 5-fold cross validation to build the model. The test results indicate that the model can achieve a high precision (98%) and recall (100%), the F1-score can reach 99%, and that the implementation of each sample test needs only 7 ms, it has provided a feasible approach for the future big-data analysis of pulsars. 相似文献
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