TOD: Trash Object Detection Dataset

Min Seok Jo, Seong Soo Han, Chang Sung Jeong

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we produce Trash Object Detection (TOD) dataset to solve trash detection problems. A wellorganized dataset of sufficient size is essential to train object detection models and apply them to specific tasks. However, existing trash datasets have only a few hundred images, which are not sufficient to train deep neural networks. Most datasets are classification datasets that simply classify categories without location information. In addition, existing datasets differ from the actual guidelines for separating and discharging recyclables because the category definition is primarily the shape of the object. To address these issues, we build and experiment with trash datasets larger than conventional trash datasets and have more than twice the resolution. It was intended for general household goods. And annotated based on guidelines for separating and discharging recyclables from the Ministry of Environment. Our dataset has 10 categories, and around 33K objects were annotated for around 5K images with 1280×720 resolution. The dataset, as well as the pre-trained models, have been released at https://github.com/jms0923/tod

Original languageEnglish
Pages (from-to)524-534
Number of pages11
JournalJournal of Information Processing Systems
Volume18
Issue number4
DOIs
Publication statusPublished - 2022 Aug

Keywords

  • Dataset
  • Deep learning
  • Recognition
  • Trash detection

ASJC Scopus subject areas

  • Software
  • Information Systems

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