论文标题
空中图像像素级分段
Aerial Imagery Pixel-level Segmentation
论文作者
论文摘要
空中图像可用于全球范围内的重要工作。然而，使用神经网络架构对这些数据进行分析落后于流行数据集的当前最新数据，例如Pascal VOC，CityScapes和Camvid。在本文中，我们弥合了这些流行数据集和空中图像数据之间的性能差距。在多级环境中，使用最先进的神经网络体系结构在航空影像上完成的工作很少。我们有关数据扩展，标准化，图像大小和损耗功能的实验，可以洞悉空中成像分割数据集的高性能设置。我们的工作是使用最新的DeepLabv3+ Xception65体系结构的工作，在Dronedeploy验证集上达到了70％的平均值。通过此结果，我们显然优于当前公开可用的最先进的验证集Miou（65％）的性能，占5％。此外，据我们所知，测试集没有MIOU基准。因此，我们还使用最佳性能DeepLabv3+ Xpection65体系结构在Dronedeploy测试集上提出了一个新的基准测试，MIOU得分为52.5％。
Aerial imagery can be used for important work on a global scale. Nevertheless, the analysis of this data using neural network architectures lags behind the current state-of-the-art on popular datasets such as PASCAL VOC, CityScapes and Camvid. In this paper we bridge the performance-gap between these popular datasets and aerial imagery data. Little work is done on aerial imagery with state-of-the-art neural network architectures in a multi-class setting. Our experiments concerning data augmentation, normalisation, image size and loss functions give insight into a high performance setup for aerial imagery segmentation datasets. Our work, using the state-of-the-art DeepLabv3+ Xception65 architecture, achieves a mean IOU of 70% on the DroneDeploy validation set. With this result, we clearly outperform the current publicly available state-of-the-art validation set mIOU (65%) performance with 5%. Furthermore, to our knowledge, there is no mIOU benchmark for the test set. Hence, we also propose a new benchmark on the DroneDeploy test set using the best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52.5%.
