Faster Bounding Box Annotation for Object Detection in Indoor Scenes
This work addresses the time-consuming annotation process for object detection datasets, particularly in indoor scenes, but it is incremental as it builds on existing annotation methods and models.
The paper tackles the problem of reducing manual workload in bounding box annotation for object detection by proposing a two-stage approach that uses a model trained on initial manual annotations to propose annotations for the rest, and introduces a new indoor scene dataset with more categories and varied conditions. They experimentally determine the optimal split to minimize workload and release the dataset publicly.
This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations. We experimentally study which first/second stage split minimizes to total workload. In addition, we introduce a new fully labeled object detection dataset collected from indoor scenes. Compared to other indoor datasets, our collection has more class categories, different backgrounds, lighting conditions, occlusion and high intra-class differences. We train deep learning based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy. The fully annotated dataset is released freely available for the research community.