Name
Abstract | ||
|---|---|---|
In this paper, we produce Trash Object Detection (TOD) dataset to solve trash detection problems. A well-organized 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 1280x720 resolution. The dataset, as well as the pre-trained models, have been released at https://github.com/jms0923/tod. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3745/JIPS.02.0178 | JOURNAL OF INFORMATION PROCESSING SYSTEMS |
Keywords | DocType | Volume |
Dataset, Deep Learning, Recognition, Trash Detection | Journal | 18 |
Issue | ISSN | Citations |
4 | 1976-913X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min-Seok Jo | 1 | 0 | 0.34 |
| Seong-Soo Han | 2 | 0 | 0.34 |
| Chang-Sung Jeong | 3 | 172 | 35.88 |