• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.322-330
• /
• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.1
• /
• pp.39-46
• /
• 2019

In this study, before system development for the elimination of foreign fish species, we propose an algorithm to classify fish species by training fish images with CNN. The raw data for CNN learning were directly captured images for each species, Dataset 1 increases the number of images to improve the classification of fish species and Dataset 2 realizes images close to natural environment are constructed and used as training and test data. The classification performance of four CNNs are over 99.97% for dataset 1 and 99.5% for dataset 2, in particular, we confirm that the learned CNN using Data Set 2 has satisfactory performance for fish images similar to the natural environment. And among four CNNs, AlexNet achieves satisfactory performance, and this has also the shortest execution time and training time, we confirm that it is the most suitable structure to develop the system for the elimination of foreign fish species.

• Proceedings of the Korean Society of Fisheries Technology Conference
• /
• 2000.05a
• /
• pp.294-295
• /
• 2000

양식 어류의 건강 증진을 위한 천연물질 또는 미생물의 개발과 이용에 대한 관심은 점점 높아지는 추세에 있으며, 이것은 양식산업의 친환경적 이미지 부각과 소비자에 대한 양식어의 선호도 증가 등의 장점을 향상시키는데 기여하기 때문이다. 어류의 양식과 관련한 미생물의 이용은 그 중요성에 비해 그다지 많은 연구가 수행 되지 않고 있으며, 어류의 자치어에 대한 먹이생물로서, 또는 어류의 초기먹이생물의 배양 등과 관련한 연구가 중심이 되고 있다(소림, 1966; Okamoto et al., 1988). (중략)

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.317-317
• /
• 2023

수자원의 확보와 어류 서식 환경보전이라는 두 가지 측면은 수자원 이용이라는 목적하에 합의되기 어려운 문제를 가지고 있다. 점차적으로 수자원이 고갈되는 현시점에 효율적 물관리를 위해 최근에는 단절된 하천을 연결하기 위한 기술들이 개발되고 있으며, 이중 안정적인 물공급 시스템을 구축하기 위한 수중터널이 그러한 기술 중 하나이다. 수자원의 확보 측면에서는 의미가 있는 방법일 수 있으나 물리적으로 동떨어져 있던 서로 다른 환경을 연결하는 문제로 인한 부작용이 발생하는데 이중 외래어종 유입으로 인한 수중생물의 환경변화가 가장 큰 문제로 대두되고 있다. 이러한 문제를 방지하기 위해 어류의 이동을 차단하는 기술 또한 개발되고 있으며 그물망, 버블스크린, 빛, 소리를 이용한 다양한 방법을 통해 어류의 접근을 방지하고자 노력하고 있다. 본 연구에서는 이러한 차단시설 중 기포를 이용한 버블스크린과 관련하여 효율성을 높이기 위한 방법을 모색하고자 실제 버블의 상승속도에 영향을 미치는 여러 가지 불확실성 인자들에 대한 상관성과 인과관계를 검토하고자 하였다. 이를 위해서 외부적 원인에 대한 오차요소를 확인하고 결과값에 영향을 미치는 여러 가지 변수에 대하여 검토하고자 하는 실험연구를 수행하였다. 버블실험을 위한 수로는 길이 15m, 폭 1.5m 의 제원을 가지는 직선수로에서 수행하였으며, 버블 발생을 위한 튜브는 PVC 재질이며, 5cm 간격으로 1mm 직경을 갖는 파이프를 제작하여 활용하였다. 실험의 분석은 이미지를 이용한 방법을 사용하였으며 분석위치 및 버블사이즈 그리고 해석격자 크기에 대한 상관분석 및 회귀분석을 통해 각각 인자들과의 관계를 규명하고자 하였다.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.552-560
• /
• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• The Journal of the Korea Contents Association
• /
• v.22 no.3
• /
• pp.71-80
• /
• 2022

In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.62-68
• /
• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.157-162
• /
• 2009

As the improvement of computer performance and the development of IT technology have influence upon publishing industry, the e-Book system, a type of electronic resources, has come out. This is a method to use both texts and 2D image illustrations. Nowadays, there are more and more users who want high quality contents and a variety of contents such as the illustrated fish book using the 3D system. This article adds 3D animation objects to the current e-Book system and designs and realizes the 3D fish simulation system to which e-Book technology is applied so as to improve the comprehension of texts and readability of detailed information and increase readers' immersion as well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.161-164
• /
• 2001

본 연구는 영상 이미지로 가공물을 획득하는 가공기에서 기계적인 충격이나 그 외의 요인으로 인한 가공기의 좌표공간과 CCD(Charge couple Device)카메라 상의 획득영상과의 좌표의 변화가 생기게 된다. 이에 그러한 변화에 둔감하고 유연한 좌표변환 계수를 제어기가 획득하게 함으로써 시스템의 신뢰성을 향상시키는데 그 목적이 있다. 본 논문은 이러한 좌표왜곡에 대한 수정 파라메터를 퍼지 논리에 의해 수정 하도록 하고 이를 어류가공기에 적용하여 그 효과가 나오도록 하는데 초점을 두었다.

• Journal of KIISE:Databases
• /
• v.30 no.4
• /
• pp.362-371
• /
• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.