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- Title
경량화된 임베디드 시스템에서 의미론적인 픽셀 분할 마스킹을 이용한 효율적인 영상 객체 인식 기법.
- Authors
윤희지; 박대진
- Abstract
AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.
- Subjects
OBJECT recognition algorithms; ARTIFICIAL neural networks; DEEP learning; IMAGE processing; ARTIFICIAL intelligence; OBJECT recognition (Computer vision)
- Publication
Journal of the Korea Institute of Information & Communication Engineering, 2022, Vol 26, Issue 6, p813
- ISSN
2234-4772
- Publication type
Article
- DOI
10.6109/jkiice.2022.26.6.813