IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Recyclable Objects Detection via Bounding Box CutMix and Standardized Distance-based IoU

Bounding Box CutMix와 표준화 거리 기반의 IoU를 통한 재활용품 탐지

  • Received : 2022.07.11
  • Accepted : 2022.10.11
  • Published : 2022.10.31
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Abstract

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터지원사업 (IITP-2022-2020-0-01808*)의 연구결과로 수행되었으며, 또한 정부 (과학기술정보통신부)의 재원으로 한국연구재단 (2020R1C1C100742311)의 지원을 받아 수행된 연구임.

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