Exploitation of Open Source Datasets and Deep Learning Models for the Detection of Objects in Urban Areas

In this work we utilize different open-source datasets and deep learning models for detecting objects from image data captured by a mobile mapping system integrating the multi-camera Ladybug 5+¹ in an urban area. In our experiments we exploit sets of pre-trained models and models trained via transfer learning techniques with available open source datasets for object detection, semantic-, instance-, and panoptic segmentation. Tests with the trained models are performed with image data from the Ladybug 5+ camera.