JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
TOD: Trash Object Detection Dataset |
| 영문제목(English Title) |
TOD: Trash Object Detection Dataset |
| 저자(Author) |
Min-Seok Jo
Seong-Soo Han
Chang-Sung Jeong
|
| 원문수록처(Citation) |
VOL 18 NO. 04 PP. 0524 ~ 0534 (2022. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English 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 round 5K images with 1280×720 resolution. The dataset, as well as the pre-trained models, have been released at https://github.com/jms0923/tod. |
| 키워드(Keyword) |
Dataset
Deep Learning
Recognition
Trash Detection
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| 파일첨부 |
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