ResMobileNet 모델을 활용한 교정 선별 기법 연구

• 영문명: A Letter Screening Method for Correctional Institutions Using the ResMobileNet Model
• 발행기관: 한국컴퓨터게임학회
• 저자명: 김혜진(Hye-jin Kim) 조기현(Ki-hyeon Cho) 조영서(Young-seo Cho)
• 간행물 정보: 『한국컴퓨터게임학회논문지』제38권 3호, 37~48쪽, 전체 12쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2025.07.31

국문 초록

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

1. 서론

2. 관련 연구 분석

3. 연구 방법

4. 연구 결과

5. 결론 및 향후 연구 방향

참고문헌

키워드

Deep Learning Letter Screening Correctional Institutions Lightweight CNN Models