Supplemental Synthetic Images (outdated)

OverviewThis is a set of synthetic overhead
imagery of wind turbines that
was created with CityEngine. There are
corresponding labels that provide
the class, x and y coordinates, and height and width (YOLOv3 format) of
the ground truth bounding boxes for each wind turbine in the images.
These labels are named similarly to the images (e.g. image.png will have the label titled image.txt)..UseThis
dataset is meant as supplementation to training an object detection
model on overhead images of wind turbines. It can be added to the
training set of an object detection model to potentially improve
performance when using the model on real overhead images of wind
turbines.WhyThis
dataset was created to examine the utility of adding synthetic imagery
to the training set of an object detection model to improve performance
on rare objects. Since wind turbines are both very rare in number and
sparse, this makes acquiring data very costly. This synthetic imagery is
meant to solve this issue by automating the generation of new training
data. The use of synthetic imagery can also be applied to the issue of
cross-domain testing, where the model lacks training data on a
particular region and consequently struggles when used on that region.MethodThe
process for creating the dataset involved selecting background images from NAIP imagery available on Earth OnDemand. These images were randomlyselected from these geographies: forest, farmland, grasslands, water, urban/suburban,mountains, and deserts. No consideration was put into whether the background images would seem realistic. This is because we wanted to see if this would help the model become better at detecting wind turbines regardless of their context (which would help when using the model on novel geographies). Then, a script was used to select these at random and
uniformly generate 3D models of large wind turbines over the image and
then position the virtual camera to save four 608x608 pixel images. This
process was repeated with the same random seed, but with no background
image and the wind turbines colored as black. Next, these black and
white images were converted into ground truth labels by grouping the
black pixels in the images.

概述本数据集由 CityEngine 创建的合成风电场高空影像组成。其中包含相应的标签，这些标签提供了每个图像中风电场的类别、x 和 y 坐标、以及高度和宽度（YOLOv3 格式）的地面真实边界框。这些标签的命名与图像相似（例如，image.png 将具有名为 image.txt 的标签）。用途此数据集旨在作为训练对象检测模型在风电场高空影像上的补充。它可以添加到对象检测模型的训练集中，以潜在地提高模型在处理真实风电场高空影像时的性能。原因创建此数据集的目的是为了检验将合成影像添加到对象检测模型训练集中以提高罕见对象检测性能的实用性。鉴于风电场数量稀少且分布稀疏，获取此类数据成本高昂。这些合成影像旨在通过自动化生成新的训练数据来解决这一问题。合成影像的使用还可以应用于跨域测试的问题，即模型在特定区域缺乏训练数据，因此在该区域使用时表现不佳。方法创建数据集的过程涉及从 Earth OnDemand 上可用的 NAIP 影像中选择背景图像。这些图像从以下地理区域中随机选取：森林、农田、草原、水域、城市/郊区、山脉和沙漠。并未考虑背景图像的真实性。这是因为我们希望了解这能否帮助模型在不受其背景（有助于在新型地理区域使用模型时）影响的情况下更好地检测风电场。随后，使用脚本随机选择这些图像，并均匀地生成覆盖图像的大型风电场的 3D 模型，然后调整虚拟相机以保存四个 608x608 像素的图像。使用相同的随机种子重复此过程，但无背景图像，并将风电场着色为黑色。接下来，将这些黑白图像通过将图像中的黑色像素分组转换为地面真实标签。