Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Korea.
Department of Electronics & Control Engineering, Hanbat National University, Daejeon 34158, Korea.
Sensors (Basel). 2020 Mar 20;20(6):1737. doi: 10.3390/s20061737.
This paper proposes a novel method of semantic segmentation, consisting of modified dilated residual network, atrous pyramid pooling module, and backpropagation, that is applicable to augmented reality (AR). In the proposed method, the modified dilated residual network extracts a feature map from the original images and maintains spatial information. The atrous pyramid pooling module places convolutions in parallel and layers feature maps in a pyramid shape to extract objects occupying small areas in the image; these are converted into one channel using a 1 × 1 convolution. Backpropagation compares the semantic segmentation obtained through convolution from the final feature map with the ground truth provided by a database. Losses can be reduced by applying backpropagation to the modified dilated residual network to change the weighting. The proposed method was compared with other methods on the Cityscapes and PASCAL VOC 2012 databases. The proposed method achieved accuracies of 82.8 and 89.8 mean intersection over union (mIOU) and frame rates of 61 and 64.3 frames per second (fps) for the Cityscapes and PASCAL VOC 2012 databases, respectively. These results prove the applicability of the proposed method for implementing natural AR applications at actual speeds because the frame rate is greater than 60 fps.
本文提出了一种新的语义分割方法，该方法由改进的空洞残差网络、空洞金字塔池化模块和反向传播组成，适用于增强现实（AR）。在提出的方法中，改进的空洞残差网络从原始图像中提取特征图并保持空间信息。空洞金字塔池化模块并行放置卷积并将特征图分层为金字塔形状，以提取图像中占据小区域的对象；这些对象使用 1×1 卷积转换为一个通道。反向传播将从最终特征图卷积获得的语义分割与数据库提供的地面真值进行比较。通过将反向传播应用于改进的空洞残差网络来改变权重，可以减少损失。在 Cityscapes 和 PASCAL VOC 2012 数据库上，将提出的方法与其他方法进行了比较。对于 Cityscapes 和 PASCAL VOC 2012 数据库，提出的方法的平均交并比（mIOU）分别为 82.8 和 89.8，帧率分别为 61 和 64.3 帧/秒（fps）。这些结果证明了所提出的方法在实际速度下实现自然 AR 应用的适用性，因为帧率大于 60 fps。
