Intelligent Underwater Stereo Camera Design for Fish Metric Estimation Using Reliable Object Matching

Ubina, Naomi A.; Cheng, Shyi-Chyi; Chang, Chin-Chun; Cai, Sin-Yi; Lan, Hsun-Yu; Lu, Hoang-Yang

doi:10.1109/access.2022.3185753

Cited by 21 publications

(14 citation statements)

References 63 publications

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“…In our previous work, we integrated a low-cost stereo camera system to perform fish metrics estimation. We used a reliable object-based matching using sub-pixel disparity computation with video interpolation CNN and tracked and computed the fish length in each video frame [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

“…For aquaculture farms, it is vital to monitor the fish growth and population as an essential parameter to approximate fish food and assess the overall wellness of the fish species. To achieve the goal of smart aquaculture, fish counting and body length estimation using underwater images are essential to estimate the fish growth curve [ 9 , 10 ]. Cameras as sensors can now be used to capture underwater fish images in an off-shore cage in a non-intrusive manner that reduces the manual handling of the fish, thus reducing direct contact that can cause stress, injury, and growth disturbance to the fish species in the cage.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…Furthermore, the AIoT tools facilitate intelligent farm management, improve aquaculture operation efficiency, control production quality, and link with the food supply chain and decision support tools [27]. Underwater cameras or sonar imaging devices are essential sensors to capture underwater images, inputted to specific AI functions, and big data analytics to extract management knowledge from fish farms [28][29][30][31][32]. For instance, fish health, growth, and behavior detection can be monitored and controlled using advanced AI and deep learning techniques [33,34].…”

Section: Literature Reviewsmentioning

confidence: 99%

Digital Twin Architecture Evaluation for Intelligent Fish Farm Management Using Modified Analytic Hierarchy Process

et al. 2022

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Precision aquaculture deploys multi-mode sensors on a fish farm to collect fish and environmental data and form a big collection of datasets to pre-train data-driven prediction models to fully understand the aquaculture environment and fish farm conditions. These prediction models empower fish farmers for intelligent decisions, thereby providing objective information to monitor and control factors of automatic aquaculture machines and maximize farm production. This paper analyzes the requirements of a digital transformation infrastructure consisting of five-layered digital twins using extensive literature reviews. Thus, the results help realize our goal of providing efficient management and remote monitoring of aquaculture farms. The system embeds cloud-based digital twins using machine learning and computer vision, together with sensors and artificial intelligence-based Internet of Things (AIoT) technologies, to monitor fish feeding behavior, disease, and growth. However, few discussions in the literature concerning the functionality of a cost-effective digital twin architecture for aquaculture transformation are available. Therefore, this study uses the modified analytical hierarchical analysis to define the user requirements and the strategies for deploying digital twins to achieve the goal of intelligent fish farm management. Based on the requirement analysis, the constructed prototype of the cloud-based digital twin system effectively improves the efficiency of traditional fish farm management.

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“…However, these methods can be limited in their ability to track large groups of fish or to track fish behaviour in three dimensions. To overcome these limitations, researchers have started to use stereo cameras to track fish behaviour [13]. Stereo cameras use two or more cameras to capture images of the same scene from different angles.…”

Section: Introductionmentioning

confidence: 99%

StereoYolo+DeepSORT: a framework to track fish from underwater stereo camera in situ

Saad,

Jakobsen,

Bondø

et al. 2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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This paper presents a 3D multiple object detection and tracking framework for identifying and quantifying changes in fish behaviour through tracking the 3D position, distance and speed of fish with respect to an underwater stereo camera. The framework consists of six essential modules based on 3D object detection to identify fish and multiple object tracking algorithms to track the fish in sequential frames. In particular, the latest version of Yolo (Yolov7) is utilised for object detection and the deep SORT algorithm is used for multiple object tracking. The framework was tested using videos captured from an underwater stereo camera in an industrial-scale sea-based fish farm. The results showed that the framework was able to accurately detect and track multiple fish in 3D. The fish position, distance and speed relative to the camera were also successfully detected. The results of this study demonstrate the effectiveness of this framework in identifying and quantifying changes in fish behaviour. The proposed novel framework has the potential to greatly enhance our understanding of fish behaviour in their natural habitats, leading to new insights into fish ecology and behaviour, while at the same time, it can enable researchers to study fish behaviour in a more detailed and accurate way.

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Intelligent Underwater Stereo Camera Design for Fish Metric Estimation Using Reliable Object Matching

Cited by 21 publications

References 63 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

Digital Twin Architecture Evaluation for Intelligent Fish Farm Management Using Modified Analytic Hierarchy Process

StereoYolo+DeepSORT: a framework to track fish from underwater stereo camera in situ

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