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arXiv 2003.007102020-03-09cs.CV

Learned Enrichment of Top-View Grid Maps Improves Object Detection

FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院 (KIT)）；Mobile Perception Systems Group（移动感知系统小组）；Institute of Measurement and Control Systems（测量与控制系统研究所）；Mobile Perception Systems Group, FZI Research Center for Information Technology, Karlsruhe, Germany（德国卡尔斯鲁厄FZI信息技术研究中心移动感知系统小组）；Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany（德国卡尔斯鲁厄卡尔斯鲁厄理工学院 (KIT) 测量与控制系统研究所）

Sascha Wirges, Ye Yang, Sven Richter, Haohao Hu, Christoph Stiller

备注：: 6 pages, 6 figures, 4 tables

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英文摘要：

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.

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