Object detection is increasingly used onboard Unmanned Aerial Vehicles (UAV) for various applications; however, the machine learning (ML) models for UAV-based detection are often validated using data curated for tasks unrelated to the UAV application. This is a concern because training neural networks on large-scale benchmarks have shown excellent capability in generic object detection tasks, yet conventional training approaches can lead to large inference errors for UAV-based images. Such errors arise due to differences in imaging conditions between images from UAVs and images in training. To overcome this problem, we characterize boundary conditions of ML models, beyond which the models exhibit rapid degradation in detection accuracy. Our work is focused on understanding the impact of different UAV-based imaging conditions on detection performance by using synthetic data generated using a game engine. Properties of the game engine are exploited to populate the synthetic datasets with realistic and annotated images. Specifically, it enables the fine control of various parameters, such as camera position, view angle, illumination conditions, and object pose. Using the synthetic datasets, we analyze detection accuracy in different imaging conditions as a function of the above parameters. We use three well-known neural network models with different model complexity in our work. In our experiment, we observe and quantify the following: 1) how detection accuracy drops as the camera moves toward the nadir-view region; 2) how detection accuracy varies depending on different object poses, and 3) the degree to which the robustness of the models changes as illumination conditions vary.
翻译:在无人驾驶飞行器(UAV)上越来越多地使用物体探测,用于各种应用;然而,在无人驾驶飞行器(UAV)上,无人驾驶飞行器(UAV)检测的机器学习(ML)模型往往使用与UAV应用程序无关的任务的包治数据加以验证。这是一个令人关切的问题,因为大规模基准神经网络的培训显示在通用物体探测任务方面表现出极强的能力,然而常规培训方法可能导致基于UAV的图像出现巨大的推断错误。这种错误是由于无人驾驶飞行器图像和训练中的图像在图像状况上的差异造成的。为了克服这一问题,我们确定ML模型的边界条件,超出这些模型的边界条件显示探测准确性迅速下降。我们的工作重点是通过使用一个游戏引擎生成的合成数据了解基于UAV的不同成像条件对探测性的影响。利用游戏引擎的特性将合成数据集与现实和附加说明的图像混集在一起。具体地说,它使得各种参数,如摄影机位置、视角、透视角度、照明条件和物体构成的精确性。我们利用合成数据集分析不同目标的精确性条件的探测条件,作为上面参数的精确度的精确度。 我们使用三种不同的测试模型,用不同的实验模型来测量的精确度。