# Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback

Source: [Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback](<https://www.sota2.com/research/paper/68fa759b1e209131cea68edd>)

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

Data updated: 2025-10-26T03:37:45.505Z

Authors: Takuya Kiyokawa, Naoki Shirakura, Hiroki Katayama, Keita Tomochika, Jun Takamatsu

Published: 2023-04-11T00:17:28.000Z

## Abstract

Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies have attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this, we propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, thereby amplifying motivation, we propose three types of online visual feedback features to track the progress of the collection status. Our experiments thoroughly investigated the impact of each feature on collection performance and quality of operation. The results suggested the feasibility of annotation and object detection.

## 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/2304.04901>)

[PDF](<https://arxiv.org/pdf/2304.04901.pdf>)