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首页> 外文期刊>IEEE Robotics and Automation Letters >Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation
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Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation

机译：传感器转移：学习最佳的传感器效果图像增强，实现从模拟到真实的域自适应

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摘要

Performance on benchmark datasets has drastically improved with advances in deep learning. Still, cross-dataset generalization performance remains relatively low due to the domain shift that can occur between two different datasets. This domain shift is especially exaggerated between synthetic and real datasets. Significant research has been done to reduce this gap, specifically via modeling variation in the spatial layout of a scene, such as occlusions, and scene environmental factors, such as time of day and weather effects. However, few works have addressed modeling the variation in the sensor domain as a means of reducing the synthetic to real domain gap. The camera or sensor used to capture a dataset introduces artifacts into the image data, which are unique to the sensor model, suggesting that sensor effects may also contribute to domain shift. To address this, we propose a learned augmentation network composed of physically-based augmentation functions. Our proposed augmentation pipeline transfers specific effects of the sensor model-chromatic aberration, blur, exposure, noise, and color temperature-from a real dataset to a synthetic dataset. We provide experiments which demonstrate that augmenting synthetic training datasets with the proposed learned augmentation framework reduces the domain gap between synthetic and real domains for object detection in urban driving scenes.
机译：随着深度学习的进步，基准数据集的性能已大大提高。尽管如此，由于两个不同数据集之间可能发生域移动，因此跨数据集的泛化性能仍然相对较低。在合成数据集和实际数据集之间，这种域偏移尤其明显。已经进行了大量研究来减小这种差距，特别是通过对场景的空间布局中的变化（例如遮挡）和场景环境因素（例如一天中的时间和天气影响）建模来进行建模。然而，很少有工作致力于对传感器域中的变化进行建模，以减少合成域到实际域之间的间隙。用于捕获数据集的相机或传感器将伪像引入图像数据，这是传感器模型所特有的，这表明传感器效应也可能会导致域偏移。为了解决这个问题，我们提出了一个由基于物理的增强功能组成的学习型增强网络。我们提出的增强流水线将传感器模型的特定效果（色差，模糊，曝光，噪声和色温）从真实数据集传输到合成数据集。我们提供的实验表明，使用建议的学习增强框架增强合成训练数据集可减少用于城市驾驶场景中目标检测的合成域与真实域之间的域间隙。

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