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딥러닝 기반 실시간 어류 탐지 알고리즘 구현
Implementation of Real-time Fish Detection Approach using Deep Learning
- 이용환;
- 김흥준
초록
This study proposes a deep learning-based approach for real-time fish detection under underwater environments. By integrating model compression techniques and transfer learning into a YOLOv3 framework, the system aims to achieve efficient and practical detection performance even on resource-constrained platforms. The proposed approach emphasizes adaptability to underwater imaging challenges and potential deployment in marine monitoring systems. This work contributes to the field by presenting a scalable framework for applying deep learning to real-world aquatic ecological monitoring. Furthermore, the proposed method offers potential applications in low-power semiconductor and display-based embedded systems for real-time visual detection.
키워드
Fish Detection; YOLOv3; Deep Learning; Underwater Vision; Model Pruning
- 제목
- 딥러닝 기반 실시간 어류 탐지 알고리즘 구현
- 제목 (타언어)
- Implementation of Real-time Fish Detection Approach using Deep Learning
- 저자
- 이용환; 김흥준
- 발행일
- 2025-06
- 저널명
- 반도체디스플레이기술학회지
- 권
- 24
- 호
- 2
- 페이지
- 117 ~ 122