# Faster Bounding Box Annotation for Object Detection in Indoor Scenes

Source: [Faster Bounding Box Annotation for Object Detection in Indoor Scenes](<https://www.sota2.com/research/paper/68fa75691e209131cea67ab5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-14T19:38:29.762Z

Authors: Bishwo Adhikari, Jukka Peltom\\"aki, Jussi Puura, Heikki Huttunen

Published: 2018-07-03T08:46:57.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.03142>)

[PDF](<https://arxiv.org/pdf/1807.03142.pdf>)