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Human-Machine Interaction Technology (HIT)
Car detection area segmentation using deep learning system
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- The International Journal of Advanced Smart Convergence 바로가기
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- 통권
- Volume 12 Number 4 (2023.12)바로가기
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- 페이지
- pp.182-189
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A440429
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform
목차
Abstract
1. IntrodCon
2. Background Knowledge
2-1. Definition of Deep learning.
2-2 Definition of object segmentation.
3. Suggestion System
3-1. Setup environment
3-2. System structure
3-3. Program operating
4. Object detection result.
5. Conclusion
Acknowledgement
References
키워드
Image processing QT Deep learning segmentation Object detection.저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- The International Journal of Advanced Smart Convergence
- 간기
- 계간
- pISSN
- 2288-2847
- eISSN
- 2288-2855
- 수록기간
- 2012~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / The International Journal of Advanced Smart Convergence Volume 12 Number 4
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