Automated fish fry counting and schooling behavior analysis using computer vision

Labuguen, Rollyn; Volante, Edsel Jose P.; Causo, Albert; Bayot, Roy Khristopher O.; Peren, G.; Macaraig, R. M.; Libatique, Nathaniel Joseph C.; Tangonan, Gregory L.

doi:10.1109/cspa.2012.6194729

Cited by 29 publications

(20 citation statements)

References 6 publications

(8 reference statements)

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“…Due to advances in computer vision and imaging technologies, the research of computer-aided image analysis have attracted signicant attention in aquaculture industry in recent years [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]. One of the main research focuses is to improve management and practices in a hatchery operation which is central to the industry.…”

Section: Introductionmentioning

confidence: 99%

“…Some are semi-automated methods [1], [8] which requires human intervention and manually fish fry handling. Moreover, most existing fully-automated fry counting systems [2], [9], [10], [11] have not paid much attention to a problem of fry overlapping in a population image, especially when fry under investigation are allowed to swim freely, resulting in significant counting and measuring errors [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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A skeleton reconstruction algorithm for identifying individual fish fry in a population image

Boonchuaychu

Wattuya

Taparhudee

2015

2015 IEEE International Conference on Imaging Systems and Techniques (IST)

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Due to rapid advance of computer vision technology, computer assisted image analysis starts to play an important role in several areas including aquaculture. In recent years several computer vision-based methods have been applied to many major operations, e.g. automated fish counting, inspection, and measurement. In this paper we address a problem of overlapping objects in a population image that frequently occurs when objects under investigation are allowed to move freely during operations. We proposed a new skeleton reconstruction algorithm for identifying and isolating individual objects in a cluster of overlapping objects. The algorithm re-assembles initial skeleton of an object cluster based on combination of edge and geometric measures, in order to form correct skeletons of individual objects in a cluster. Skeletons produced by our algorithm will be used as a basis for further automated inspection and measurement tasks. In this paper we apply our algorithm in a field of aquaculture for automated identifying individual fish fry in an overlappingfry cluster. Our algorithm can achieve 93.33 percent accuracy for skeleton reconstruction of each individual fry in a cluster of 2 -7 overlapping fry. The results also show the effectiveness of our algorithm in dealing with various overlapping patterns.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

Boonchuaychu

Wattuya

Taparhudee

2015

2015 IEEE International Conference on Imaging Systems and Techniques (IST)

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“…For example, information of the blobs was used to count fish fry [7] but the size of fry needs to be kept basically the same. Similarly, the area information of the outline was used to count fish [8], but the water level must be kept shallow to avoid overlapping. By extracting seven shape features, the least square support vector machine (LSSVM) achieves 98.73% accuracy for fish fry counting [9].…”

Section: Introductionmentioning

confidence: 99%

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Zhang

Yang

Wang

et al. 2020

Animals

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In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

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“…Despite some of the disadvantages of Fish counting using DIP, it is still a viable solution in catfish farms as it would reduce counting time, minimize fish stress, and facilitated proper financial planning [12]. Thus, to harness the benefit of DIP, one of the major issues to improve in fish counting is accuracy, which is affected by factors such as noise, overlapped and other objects other than fish in the pond [13,14]. As the DIP basically employed pixel counting, there is a problem of object recognition to substantiate which area pixel is associated with fish as noise or variation in illumination or any other object in the image could erroneously be considered as fish.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Recognition in Catfish Counting Algorithm Using Artificial Neural Network and Geometry

2020

KSII TIIS

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One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.

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Automated fish fry counting and schooling behavior analysis using computer vision

Cited by 29 publications

References 6 publications

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Incorporating Recognition in Catfish Counting Algorithm Using Artificial Neural Network and Geometry

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