为解决图像分类任务中模型结构固化、产生巨大内存消耗、时间消耗的问题,提出一种增量式深度神经网络(IDNN)。输入样本通过聚类算法激活不同簇并被分别处理:如果新样本激活已有簇,则更新该簇参数;否则为新簇开辟分支,并训练独立特征集。...为解决图像分类任务中模型结构固化、产生巨大内存消耗、时间消耗的问题,提出一种增量式深度神经网络(IDNN)。输入样本通过聚类算法激活不同簇并被分别处理:如果新样本激活已有簇,则更新该簇参数;否则为新簇开辟分支,并训练独立特征集。在Caltech-101、ORL Face、ETH-80数据库的验证结果表明,该系统能自动调整网络结构,适用于轮廓、纹理、视角等不同环境的增量式学习,例如在Caltech-101库分类任务中准确率超出VGGNet 5.08%、Alex Net 3.44%。展开更多
In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convo...In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.展开更多
文摘为解决图像分类任务中模型结构固化、产生巨大内存消耗、时间消耗的问题,提出一种增量式深度神经网络(IDNN)。输入样本通过聚类算法激活不同簇并被分别处理:如果新样本激活已有簇,则更新该簇参数;否则为新簇开辟分支,并训练独立特征集。在Caltech-101、ORL Face、ETH-80数据库的验证结果表明,该系统能自动调整网络结构,适用于轮廓、纹理、视角等不同环境的增量式学习,例如在Caltech-101库分类任务中准确率超出VGGNet 5.08%、Alex Net 3.44%。
基金National Defense Pre-research Fund Project(No.KMGY318002531)。
文摘In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.
