论文标题
边缘设备上有效的语义分割
Efficient Semantic Segmentation on Edge Devices
论文作者
论文摘要
语义细分在计算机视觉算法上起作用，可将图像的每个像素分配给类。语义分割的任务应以准确性和效率既有。现有的大多数深FCN会产生重型计算，这些网络非常饥饿，不适合在便携式设备上实时应用。该项目分析了当前的语义分割模型，以探索在灾难性事件期间将这些模型应用于紧急响应的可行性。我们将实时语义分割模型的性能与在对立设置下受到空中图像约束的非真实时间对应物的性能。此外，我们在洪水网络数据集上训练了几个型号，其中包含飓风哈维飓风后捕获的无人机图像，并在特殊阶级上进行了执行，例如被洪水泛滥的建筑物与非贫民的建筑物或洪水泛滥的道路与非洪水道路相比。在这个项目中，我们开发了一个基于UNET的实时模型，并在Jetson Agx Xavier模块上部署了该网络。
Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.
