Faster Bounding Box Annotation for Object Detection in Indoor Scenes

TL;DR

This paper introduces a two-stage method for rapid bounding box annotation in indoor scene datasets, combining manual annotation with model-assisted labeling, and provides a new diverse indoor dataset for object detection.

Contribution

It presents a novel two-stage annotation approach and releases a comprehensive indoor object detection dataset with diverse conditions.

Findings

The two-stage annotation process reduces workload significantly.

The new dataset contains more classes and varied indoor conditions.

Deep learning detectors achieve competitive speed and accuracy on the dataset.

Abstract

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.