딥러닝 기반의 객체 탐지 모델을 활용한 어종 및 어병 탐지 시스템 (Fish Species and Disease Detection System Using Deep Learning-Based Object Detection Model)

서지정보

· 발행기관 : 한국멀티미디어학회
· 수록지 정보 : 멀티미디어학회논문지 / 26권 / 8호 / 898 ~ 910페이지
· 저자명 : 배명훈, 박준, 정세훈, 심춘보

초록

In fish farms, the overuse of feed can lead to residue and fish excrement polluting the water quality environment, thereby increasing the probability of pathogen proliferation and disease incidence in fish. In order to minimize the occurrence of diseases, it is crucial to administer an appropriate amount of feed and manage breeding diligently while mitigating any stress factors affecting the fish. This study involves the development of a fish species and disease detection system, where models are trained to identify different types of fish and their diseases. The system is designed to be used by fish farmers, offering a user-friendly interface through the Web. In the model training, the YOLOv7 model demonstrated high performance, achieving over 0.9 accuracy in detecting fish species. Meanwhile, for fish disease detection, the YOLOv5l model exhibited overall superior performance. However, there was a limitation in the dataset for fish disease detection, with only a small number of samples available. To overcome this, the fish species and disease detection system, developed in conjunction with the YOLOv5l model, was incorporated into the web page. This system aims to help identify the species, disease status, and specific affected regions in the fish population.

영어초록

In fish farms, the overuse of feed can lead to residue and fish excrement polluting the water quality environment, thereby increasing the probability of pathogen proliferation and disease incidence in fish. In order to minimize the occurrence of diseases, it is crucial to administer an appropriate amount of feed and manage breeding diligently while mitigating any stress factors affecting the fish. This study involves the development of a fish species and disease detection system, where models are trained to identify different types of fish and their diseases. The system is designed to be used by fish farmers, offering a user-friendly interface through the Web. In the model training, the YOLOv7 model demonstrated high performance, achieving over 0.9 accuracy in detecting fish species. Meanwhile, for fish disease detection, the YOLOv5l model exhibited overall superior performance. However, there was a limitation in the dataset for fish disease detection, with only a small number of samples available. To overcome this, the fish species and disease detection system, developed in conjunction with the YOLOv5l model, was incorporated into the web page. This system aims to help identify the species, disease status, and specific affected regions in the fish population.

참고자료

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