Object detection aims to locate objects in images by classifying them and assigning them bounding boxes. However, this representation is insufficient when details of the object geometry are of interest. In such cases, more computationally intense methods such as instance segmentation are necessary. Furthermore, bounding boxes do not distinguish heavily occluding objects. In this work, we propose a more geometrically descriptive representation, a centerline. We train an object detection model to predict centerlines instead of bounding boxes. This allows us to obtain some geometric properties of the objects directly during detection, without the need for segmentation. We also find the proposed approach to improve detection performance over the bounding-box-based baseline.