Image Semantic Segmentation using Deep Convolutional Nets, Fully Connected Conditional Random Fields, and Dilated Convolution
首发时间:2019-04-04
Abstract:Deep convolutional neural networks (DCNNs) have recently demonstrated state-of-the-art performance in advanced vision tasks, such as image classification and object detection. This work focuses on solving image semantic segmentation tasks. First,we combine a new feature extraction network with adilated convolution layer to improve the accuracy of the model's mission. Second, we introduce multi-scale feature fusion technology to improve the performance of DCNN. Third, we combine the DCNN with fully connected conditional random field to overcome the inaccurate positioning of DCNN and optimize their output. Our approach is demonstrated on the PASCAL VOC-2012 Image Semantic Segmentation dataset, where 78.1% IOU accuracy is achieved in the test set. Our approach can compute neural network responses intensively at 9 frames per second on modern GPUs.
keywords: Software engineering Image Semantic segmentation Fully connected conditional random fields Dilated convolutions
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使用深度卷积网络,全连接条件随机场和扩张卷积的图像语义分割
摘要:深度卷积神经网络(DCNN)最近在高级视觉任务中展示了最先进的性能,例如图像分类和物体检测。这项工作的重点是是解决图像语义分割任务。 首先,我们结合使用提取网络来扩展卷积层,以提高模型任务的准确性。其次,我们引入了多尺度特征融合技术来提高DCNN的性能。 第三,我们将DCNN与完全连接的条件随机场相结合,以克服DCNN的不准确定位并优化其输出。 我们的方法在PASCAL VOC-2012图像语义分割数据集上得到证明,其中在测试集中实现了78.1%的IOU准确度。 我们的方法可以在现代GPU上以每秒9帧的速度集中计算神经网络响应。
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