在无人驾驶技术中,道路场景的理解是一个非常重要的环境感知任务,也是一个很具有挑战性的课题。提出了一个深层的道路场景分割网络(Road Scene Segmentation Network,RSSNet),该网络为32层的全卷积神经网络,由卷积编码网络和反卷积解码...在无人驾驶技术中,道路场景的理解是一个非常重要的环境感知任务,也是一个很具有挑战性的课题。提出了一个深层的道路场景分割网络(Road Scene Segmentation Network,RSSNet),该网络为32层的全卷积神经网络,由卷积编码网络和反卷积解码网络组成。网络中采用批正则化层防止了深度网络在训练中容易出现的"梯度消失"问题;在激活层中采用了Maxout激活函数,进一步缓解了梯度消失,避免网络陷入饱和模式以及出现神经元死亡现象;同时在网络中适当使用Dropout操作,防止了模型出现过拟合现象;编码网络存储了特征图的最大池化索引并在解码网络中使用它们,保留了重要的边缘信息。实验证明,该网络能够大大提高训练效率和分割精度,有效识别道路场景图像中各像素的类别并对目标进行平滑分割,为无人驾驶汽车提供有价值的道路环境信息。展开更多
We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system,wh...We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system,which is suitable for repeated measurements in mass screening.Sixty-three optical tomographic images were collected from women with dense breasts,and a dataset of 12602D gray scale images sliced from these 3D images was built.After image preprocessing and normalization,we tested the network on this dataset and obtained 0.80 specificity,0.95 sensitivity,90.2%accuracy,and 0.94 area under the receiver operating characteristic curve(AUC).Furthermore,a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset.The sensitivity,specificity,accuracy,and AUC of the classification on the augmented dataset were 0.88,0.96,93.3%,and 0.95,respectively.展开更多
文摘在无人驾驶技术中,道路场景的理解是一个非常重要的环境感知任务,也是一个很具有挑战性的课题。提出了一个深层的道路场景分割网络(Road Scene Segmentation Network,RSSNet),该网络为32层的全卷积神经网络,由卷积编码网络和反卷积解码网络组成。网络中采用批正则化层防止了深度网络在训练中容易出现的"梯度消失"问题;在激活层中采用了Maxout激活函数,进一步缓解了梯度消失,避免网络陷入饱和模式以及出现神经元死亡现象;同时在网络中适当使用Dropout操作,防止了模型出现过拟合现象;编码网络存储了特征图的最大池化索引并在解码网络中使用它们,保留了重要的边缘信息。实验证明,该网络能够大大提高训练效率和分割精度,有效识别道路场景图像中各像素的类别并对目标进行平滑分割,为无人驾驶汽车提供有价值的道路环境信息。
基金This research was supported by the University of Electronic Science and Technology of ChinaChina Postdoctoral Science Foundation(No.2018M633347).
文摘We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system,which is suitable for repeated measurements in mass screening.Sixty-three optical tomographic images were collected from women with dense breasts,and a dataset of 12602D gray scale images sliced from these 3D images was built.After image preprocessing and normalization,we tested the network on this dataset and obtained 0.80 specificity,0.95 sensitivity,90.2%accuracy,and 0.94 area under the receiver operating characteristic curve(AUC).Furthermore,a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset.The sensitivity,specificity,accuracy,and AUC of the classification on the augmented dataset were 0.88,0.96,93.3%,and 0.95,respectively.
