Earticle

초록

영어

This study compares the performance of various convolutional neural network (CNN) models for building an automated deep learning-based letter screening system targeting letters received by inmates in correctional institutions. The models evaluated include well-known architectures such as MobileNet, ResNet, and Inception, as well as recently proposed lightweight models such as ResMobileNet and IGSe, along with GroupConv and SE. Each model was trained on image data containing the Korean word for "knife" ("칼") to assess performance in terms of accuracy, processing time, and model compactness. A total of 1,197 letter image samples were used in the experiment, including 1,140 images with normal words and 57 images containing the target word. The experimental results showed that the MobileNet model had the shortest processing time, making it suitable for real-time applications, while the IGSe model achieved the highest accuracy, demonstrating optimal performance for letter screening tasks. This study suggests that deep learning-based screening techniques can be effectively applied to enhance digital security in the management of inmate correspondence within correctional institutions.

목차

ABSTRACT
1. 서론
2. 관련 연구 분석
3. 연구 방법
3.1 데이터 수집 및 전처리
3.2 CNN 모델 설계 및 학습 구조
3.3 실험 환경 및 학습 프로세스
3.4 성능 평가 및 비교 지표
4. 연구 결과
4.1 Mobilenet 시뮬레이션 결과
4.2 ResNet 시뮬레이션 결과
4.3 ResMobileNet 시뮬레이션 결과
4.4 Inception 시뮬레이션 결과
4.5 GroupConv 시뮬레이션 결과
4.6 SENet 시뮬레이션 결과
4.7 IGSe 시뮬레이션 결과
5. 결론 및 향후 연구 방향
5.1 결론
5.2 향후 연구 방향
참고문헌

키워드

저자

• Hye-jin Kim [ Independent researcher, 17, Deulsapyeong 2-gil, Deokjin-gu, Jeonju-si, Jeonbuk-do, Korea ]
• Ki-hyeon Cho [ Master Course, Dept. of Computer Engineering, Chonnam National University, Yeosu Campus ] Corresponding Author
• Young-seo Cho [ Master Course, Dept. of Computer Engineering, Chonnam National University, Yeosu Campus ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793