Deep learning based fish length estimation. An application for the Mediterranean aquaculture

Voskakis, Dimitris; Makris, Alexandros; Papandroulakis, Nikos

doi:10.23919/oceans44145.2021.9705813

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Stereo camera systems also have been widely used in fish length estimations [ 39 , 40 , 41 , 42 ], using disparity information to provide 3D information about an object [ 43 , 44 , 45 ]. In aquaculture, many are now putting their interest and efforts into integrating stereo cameras for fish length and biomass estimations [ 40 , 41 , 46 ]. In our previous work, we integrated a low-cost stereo camera system to perform fish metrics estimation.…”

Section: Related Workmentioning

confidence: 99%

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

Chang

Ubina

Cheng

et al. 2022

Sensors

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Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics.

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Section: Related Workmentioning

confidence: 99%

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

Chang

Ubina

Cheng

et al. 2022

Sensors

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show abstract

“…In [ 14 ], three Regional Convolutional Neural Networks (R-CNNs) ar trained in order to estimate the total length of European sea basses, using their public images. In [ 15 ], a system of two cameras acquires synchronized images, which are utilized to extract the keypoints of fishes, through the training of a CNN. These keypoints are exploited to estimate the length of Gilthead seabreams and European seabasses.…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Fishes Using Coded Structured Light

Veinidis,

Arnaoutoglou,

Syvridis

2023

J. Imaging

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3D reconstruction of fishes provides the capability of extracting geometric measurements, which are valuable in the field of Aquaculture. In this paper, a novel method for 3D reconstruction of fishes using the Coded Structured Light technique is presented. In this framework, a binary image, called pattern, consisting of white geometric shapes, namely symbols, on a black background is projected onto the surface of a number of fishes, which belong to different species. A camera captures the resulting images, and the various symbols in these images are decoded to uniquely identify them on the pattern. For this purpose, a number of steps, such as the binarization of the images captured by the camera, symbol classification, and the correction of misclassifications, are realized. The proposed methodology for 3D reconstructions is adapted to the specific geometric and morphological characteristics of the considered fishes with fusiform body shape, something which is implemented for the first time. Using the centroids of the symbols as feature points, the symbol correspondences immediately result in point correspondences between the pattern and the images captured by the camera. These pairs of corresponding points are exploited for the final 3D reconstructions of the fishes. The extracted 3D reconstructions provide all the geometric information which is related to the real fishes. The experimentation demonstrates the high efficiency of the techniques adopted in each step of the proposed methodology. As a result, the final 3D reconstructions provide sufficiently accurate approximations of the real fishes.

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A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture

Zhao,

Qin,

et al. 2024

Artif Intell Rev

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The industrialization, high-density, and greener aquaculture requires a more precise and intelligent aquaculture management. Phenotypic and behavioral information of fish, which can reflect fish growth and welfare status, play a crucial role in aquaculture management. Stereo vision technology, which simulates parallax perception of the human eye, can obtain the three-dimensional phenotypic characteristics and movement trajectories of fish through different types of sensors. It can overcome the limitations in dealing with fish deformation, frequent occlusions and understanding three-dimension scenes compared to the traditional two-dimensional computer vision techniques. With the deep learning development and application in aquaculture, stereo vision has become a super computer vision technology that can provide more precise and interpretable information for intelligent aquaculture management, such as size estimation, counting and behavioral analysis of fish. Hence, it is very beneficial for researchers, managers, and entrepreneurs to possess a thorough comprehension about the fast-developing stereo vision technology for modern aquaculture. This study provides a critical review of relevant topics, including the four-layer application structure of stereo vision technology in aquaculture, various deep learning-based technologies used, and specific application scenarios. The review contributes to research development by identifying the current challenges and provide valuable suggestions for future research directions. This review can serve as a useful resource for developing future studies and applications of stereo vision technology in smart aquaculture, focusing on phenotype feature extraction and behavioral analysis of fish.

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Deep learning based fish length estimation. An application for the Mediterranean aquaculture

Cited by 11 publications

References 8 publications

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology

3D Reconstruction of Fishes Using Coded Structured Light

A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture

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