论文标题
学会的高视图网格地图的丰富改善了对象检测
Learned Enrichment of Top-View Grid Maps Improves Object Detection
论文作者
论文摘要
我们为顶视网格地图提出了一个对象检测器，该对象检测器还经过训练，以生成其输入的丰富版本。我们在联合模型中的目标是通过以多个相邻范围传感器测量融合的地图形式将结构知识正规化来改善概括。该培训数据可以自动生成，因此不需要手动注释。我们提出了一个证据框架来生成培训数据，研究不同的模型架构，并表明预测丰富的输入作为附加任务可以改善对象检测性能。
We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input. Our goal in the joint model is to improve generalization by regularizing towards structural knowledge in form of a map fused from multiple adjacent range sensor measurements. This training data can be generated in an automatic fashion, thus does not require manual annotations. We present an evidential framework to generate training data, investigate different model architectures and show that predicting enriched inputs as an additional task can improve object detection performance.
