论文标题
从自定义传感器设置上的顶级视图网格地图从单阶段对象检测
Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups
论文作者
论文摘要
我们介绍了在自动驾驶场景中的顶级视图网格图上的单级对象检测器的无监督域适应方法。我们的目标是在从自定义传感器数据和设置产生的网格图上训练强大的对象检测器。我们首先为基于视网膜的网格图引入一个单阶段对象检测器。然后，我们在不同特征金字塔级别的图像级和实例级域分类器中扩展模型，这些分类器以对抗性方式训练。这使我们能够为未标记的域训练强大的对象探测器。我们对我们的Nuscenes和Kitti基准进行定量评估方法，并为我们的实验工具记录的未标记测量结果提供定性域的适应结果。我们的结果表明，通过应用我们的域适应策略，可以提高未标记域的对象检测准确性。
We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios. Our goal is to train a robust object detector on grid maps generated from custom sensor data and setups. We first introduce a single-stage object detector for grid maps based on RetinaNet. We then extend our model by image- and instance-level domain classifiers at different feature pyramid levels which are trained in an adversarial manner. This allows us to train robust object detectors for unlabeled domains. We evaluate our approach quantitatively on the nuScenes and KITTI benchmarks and present qualitative domain adaptation results for unlabeled measurements recorded by our experimental vehicle. Our results demonstrate that object detection accuracy for unlabeled domains can be improved by applying our domain adaptation strategy.
