Research on Human-Machine Collaborative Annotation for Traffic Scene Data

Computer vision model using deep learning requires a lot of high-quality data for training. However, obtaining amounts of well-annotated data is too expensive. The state-of-the-art automatic annotation tools can accurately detect and segment a few objects. We bring together the annotation tools and the crowed engineering into a framework for object detection and instance-level segmentation. The input of model are the image need to annotate and the annotation constraints: precision, utility and cost. The output of the model are the set of detected objects and the set of instance-level segmentation results. The model can integrate the computer vision annotation model with manual annotation model. We validate human-machine collaborative annotation model on the Cityscapes dataset.