基于深度神经网络剪枝的两阶段遥感图像目标检测
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摘要
在高分辨率遥感图像目标检测中,受云雾、光照、复杂背景、噪声等因素影响,现有目标检测方法虚警率高、速度慢、精确度低.为此提出基于深度神经网络剪枝的两阶段目标检测(object detection based on deep pruning,ODDP)方法.首先,给出深度神经网络剪枝方法,基于深度神经网络剪枝分别提出自主学习区域提取网络算法与优化训练分类网络算法;然后,将上述两算法用于卷积神经网络,得到两阶段目标检测模型.实验结果表明,在NWPU VHR-10高分辨率遥感数据集上,相比现有目标检测方法,ODDP的检测速度和精度均有一定提升.
In the object detection of high-resolution remote-sensing images,affected by cloud,light,complex background,noise and other factors,the existing object detection method has high false alarm,low speed and low precision.So we propose a two-stage object detection method based on deep pruning.First,we propose deep pruning,and then based on the deep pruning we propose an algorithm that learns region proposal network automatically and an algorithm that we train classification network with optimizing training method.We then apply the two algorithms to convolutional neural network and get a two-stage object detection model.The experiment result shows that our method has a certain improvement on precision and speed compared with the stateofthe-art method.
In the object detection of high-resolution remote-sensing images,affected by cloud,light,complex background,noise and other factors,the existing object detection method has high false alarm,low speed and low precision.So we propose a two-stage object detection method based on deep pruning.First,we propose deep pruning,and then based on the deep pruning we propose an algorithm that learns region proposal network automatically and an algorithm that we train classification network with optimizing training method.We then apply the two algorithms to convolutional neural network and get a two-stage object detection model.The experiment result shows that our method has a certain improvement on precision and speed compared with the stateofthe-art method.
引文
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[2]Long Y,Gong Y,Xiao Z,et al.Accurate object localization in remote sensing images based on convolutional neural networks[J].IEEE Transactions on Geoscience&Remote Sensing,2017,55(5):2486-2498.
[3]Jiang Q,Cao L,Cheng M,et al.Deep neural networks-based vehicle detection in satellite images[C]//International Symposium on Bioelectronics and Bioinformatics.Piscataway:IEEE,2015:184-187.
[4]Sevo I,Avramovi c'A.Convolutional neural network based automatic object detection on aerial images[J].IEEEGeoscience&Remote Sensing Letters,2016,13(5):740-744.
[5]Wu H,Zhang H,Zhang J,et al.Typical target detection in satellite images based on convolutional neural networks[C]//IEEE International Conference on Systems,Man,and Cybernetics.Piscataway:IEEE,2016:2956-2961.
[6]Denil M,Shakibi B,Dinh L,et al.Predicting parameters in deep learning[C]//Advances in Neural Information Processing Systems.Cambridge,MA:MIT,2013:2148-2156.
[7]Xia G S,Hu J,Hu F,et al.AID:a benchmark data set for performance evaluation of aerial scene classification[J].IEEETransactions on Geoscience and Remote Sensing,2017,55(7):3965-3981.
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