论文标题
高分辨率无人机图像的端到端变更检测与GAN体系结构
End-to-End Change Detection for High Resolution Drone Images with GAN Architecture
论文作者
论文摘要
目前，通过高分辨率无人机相机，监测大面积是可行的，而不是耗时且昂贵的地面调查。在这项工作中，我们首次揭示了使用具有高分辨率无人机图像进行基础架构检查的最先进的更改检测算法的潜力。我们在太阳能电池板安装上演示了这个概念。提出了一种深入学习，数据驱动的算法，用于根据变化检测深度学习算法识别变化。我们使用条件对抗网络方法为图像中的变更检测提供了一个框架。提出的网络体系结构基于Pix2Pix GAN框架。广泛的实验结果表明，我们提出的方法的表现优于其他最新的变更检测方法。
Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection. We demonstrate this concept on solar panel installation. A deep learning, data-driven algorithm for identifying changes based on a change detection deep learning algorithm was proposed. We use the Conditional Adversarial Network approach to present a framework for change detection in images. The proposed network architecture is based on pix2pix GAN framework. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art change detection methods.
