Fish Volume Monitoring Using Stereo Vision for Fish Farms

Vale, Rodrigo Telles da Silva; Ueda, Edson Kenji; Takimoto, Rogério Y.; Martins, Thiago de Castro

doi:10.1016/j.ifacol.2020.12.232

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Non-contact measurement technology mainly uses computer vision to detect the yarn roll. It can be divided into monocular vision method, stereo vision method, and deep learning method [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

“…From the obtained 3D fire points, a three-dimensional surface rendering is performed and the fire volume estimated. Sheng, J., Zhao, H., and Bu, P. propose a four-direction global matching with a cost volume update scheme [ 21 ] to cope with textureless regions and occlusion. Experimental results show that their method is highly efficient.…”

Section: Related Workmentioning

confidence: 99%

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The Detection of Yarn Roll’s Margin in Complex Background

Wang

Shi

et al. 2023

Sensors

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Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The Detection of Yarn Roll’s Margin in Complex Background

Wang

Shi

et al. 2023

Sensors

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“…Traditionally, biomass monitoring has been accomplished somewhat crudely by calculating the average size and weight of periodically collected fish from net pens minus countable dead fish (Costa et al, 2006). Accurate estimates of fish length, width, height, girth, thickness, and mass are now possible using noninvasive underwater video‐based sensors, stereo image analysis, and agent‐based and multiagent‐based simulators to interpret the data (García‐Magariño et al, 2017; da Silva Vale et al, 2020; Y. S. Yang et al, 2011). In turn, these parameters can be extrapolated or used in individual‐based growth models to gain greater insights into the overall fish condition that are relevant to animal welfare guidance found in aquaculture certification programs (Giménez‐Candela et al, 2020).…”

Section: Monitoring Tools and Strategiesmentioning

confidence: 99%

Section: Monitoring Biotic Conditionsmentioning

confidence: 99%

Environmental monitoring tools and strategies in salmon net‐pen aquaculture

Bell¹,

Mandel²,

Brainard³

et al. 2022

Integr Envir Assess & Manag

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As global salmon production accelerates in response to higher consumer demand for seafood, so does the need for sophisticated monitoring strategies to enable and maintain ethically sound, productive, and environmentally friendly production of fish. Innovative technologies are needed to ensure proper water quality, react to unfavorable hydrodynamic conditions, monitor for changes in fish health, and minimize ecological interactions with indigenous aquatic life, including fish escapes. Automated sensors connected wirelessly to data stations, visualization aids, and acoustic and physical tagging technologies are emerging tools capable of detecting environmental stress and its associated behavioral changes in farmed fish. Computer modeling of the monitoring data collected from a single salmon farm or collection of farms sharing a data network can be used to spot environmental trends vital for anticipating some of the consequences of climate change. Environmental regulations governing salmon farming in coastal areas are becoming more stringent in response to public pressures to protect coastal and ocean resources and to provide for multipurpose use of marine resources. As net‐pen salmon aquaculture expands globally, new technologies will be essential to collect and interpret the anticipated larger volumes of data needed to meet these stringent regulatory requirements and to safeguard the high investment costs inherent in salmon farming. Integr Environ Assess Manag 2022;18:950–963. © SETAC

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Precise underwater fish measurement: A geometric approach leveraging medium regression

Gao,

Xiong,

et al. 2024

Computers and Electronics in Agriculture

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Fish Volume Monitoring Using Stereo Vision for Fish Farms

Cited by 5 publications

References 7 publications

The Detection of Yarn Roll’s Margin in Complex Background

The Detection of Yarn Roll’s Margin in Complex Background

Environmental monitoring tools and strategies in salmon net‐pen aquaculture

Precise underwater fish measurement: A geometric approach leveraging medium regression

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