심층신경망을 이용한 스마트 양식장용 어류 크기 자동 측정 시스템
Automatic Fish Size Measurement System for Smart Fish Farm Using a Deep Neural Network
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.
Abstract
To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.
참고문헌 (12)
- 황성일, "u-양식장을 적용한 I CT 기반스마트 양식장 시스템 사례 연구", 한국통신학회논문지C, 39(2), pp.173~181 (2014), 한국통신학회
- D. E. Jeong, "A Study on Water Quality Data Collection and Utilization of Machine Learning Technique in Smart Aquaculture", Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp.847~848 (2019)
- 전주현, "스마트 양식장 수조 내 용존 산소 및 온도 제어를 위한 시스템 구현", 대한임베디드공학회논문지, 16(6), pp.299~305 (2021), 대한임베디드공학회[DOI]
- 전주현, "An Implementation of DAQ and Monitoring System for a Smart Fish Farm Using Circulation Filtration System", JIPS(Journal of Information Processing Systems), 17(6), pp.1179~1190 (2021), 한국정보처리학회[DOI]
- Y. H. Lee, "Automatic Fish Size Measurement for Smart Fish Farm Using Deep Neural Network", Proceedings of IEMEK, pp.80~81 (2021)
- 신영학, "스마트 양식을 위한 딥러닝 기반 어류 검출 및 이동경로 추적", 한국콘텐츠학회 논문지, 21(1), pp.552~560 (2021), 한국콘텐츠학회[DOI]
- M. Sung, "Vision Based Real-time Fish Detection Using Convolutional Neural Network", OCEANS 2017 – Aberdeen, 1(6), pp.1~6 (2017)
- X. Li, "Fast Accurate Fish Detection and Recognition of Underwater Images with Fast R-CNN", OCEANS 2015 – MTS, 2015, pp.1(5), 1~5
- G. Chen, "Automatic Fish Classification System Using Deep Learning", 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp.24~29 (2017), IEEE
- W. Xu, "Underwater Fish Detection Using Deep Learning for Water Power Applications", 2018International Conference on Computational Science and Computational Intelligence (CSCI), pp.313~318 (2018)
- J. Redmon, "YOLOv3: An Incremental Improvement", arXiv preprint arXiv:1804.02767, ~ (2018)
- Q. C. Mao, "Mini-YOLOv3: real-time object detector for embedded applications", Ieee Access, 7, pp.33529~133538 (2019)