[논문]엣지 컴퓨팅 환경에서 적용 가능한 딥러닝 기반 라벨 검사 시스템 구현

배주원; 한병길

doi:10.14372/iemek.2022.17.2.77

[국내논문] 엣지 컴퓨팅 환경에서 적용 가능한 딥러닝 기반 라벨 검사 시스템 구현
Implementation of Deep Learning-based Label Inspection System Applicable to Edge Computing Environments 원문보기

대한임베디드공학회논문지 = IEMEK Journal of embedded systems and applications, v.17 no.2, 2022년, pp.77 - 83

배주원 (Korea Maritime and Ocean University) , 한병길 (Electronics and Telecommunications Research Institute)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (7)

그림 그림 1. 제안하는 Two-Stage 객체 검출 방법의 순서 Fig. 1. Sequence of proposed Two-Stage object detection approach
그림 그림 2. 제안하는 Two-Stage 방법의 내부 데이터 입출력 과정 Fig. 2. The Internal Data InputㆍOutput Procedure of the Proposed Two-Stage Approach
그림 그림 3. SF-YOLO 모델의 구조 Fig. 3. Structure of SF-YOLO Model
그림 그림 4. Dense Convolutional Blocks의 구조 Fig. 4. Structure of Dense Convolutional Blocks
그림 그림 5. Residual Convolutional Blocks의 구조 Fig. 5. Structure of Residual Convolutional Blocks
그림 그림 6. 라벨 데이터셋의 구조 Fig. 6. Structure of label dataset
표 표 1. Two-Stage와 One-Stage 검출 방법의 성능 비교 Table 1. Comparison of performance of Two-Stage and One-Stage detection approach

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https://github.com/AlexeyAB/darknet/

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