Current Result Document :
| 한글제목(Korean Title) |
임베디드 장비 환경에서 경량화된 객체 탐지 딥러닝 모델 비교 |
| 영문제목(English Title) |
A Comparison of Lightweight Object Detection Deep Learning Model in Embedded Environment |
| 저자(Author) |
김지나
정은지
전제성
이수안
Gina Kim
Eunji Jeong
Jaesung Jun
Suan Lee
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| 원문수록처(Citation) |
VOL 45 NO. 01 PP. 2165 ~ 2168 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Object detection, one of the representative technologies of artificial intelligence, is used in various fields such as smart cities and autonomous driving and is becoming increasingly important. Recently, in order to use artificial intelligence technology in the Edge environment, many researchers are studying how to reduce the weight of the model. In this paper, we compare YOLOv5, EfficientDet, SSD MobileNetV1, and spaghettinet models in terms of inference time. For comparative experiments, embedded devices used Google Coral Dev Board and ASUS Tinker Edge T. |
| 키워드(Keyword) |
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| 파일첨부 |
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