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구분 SCI
연도 ~ 키워드


학술지 엣지 컴퓨팅 환경에서 적용 가능한 딥러닝 기반 라벨 검사 시스템 구현
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배주원, 한병길
대한임베디드공학회논문지, v.17 no.2, pp.77-83
22ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.
KSP 제안 키워드
Character detection, Label information, Learning-based, Lightweight model, Object detection, Product image, Production process, Two-Stage, deep learning(DL), deep learning models, edge computing