A suitable ichthyometer for systemic application

Lezama-Cervantes, Carlos; Godínez-Domínguez, Enrique; Gómez-Morales, Helena; Ornelas-Lugo, Ricardo; Morales-Blake, Alejandro; Patiño-Barragán, Manuel; Tintos-Gómez, Adrián

doi:10.3856/vol45-issue5-fulltext-1

Cited by 3 publications

(2 citation statements)

References 25 publications

(52 reference statements)

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“…Measurement using digital imagery is a growing field and has been successfully implemented with both single image (e.g. Lezama-Cervantes et al, 2017;Monkman et al, 2019;Andrialovanirina et al, 2020;Wibisono et al, 2022), and stereo image (e.g. Johansson et al, 2008;Shafait et al, 2017;Suo et al, 2020;Marrable et al, 2022).…”

Section: The Move Toward Automationmentioning

confidence: 99%

Deep Learning for Marine Science

2024

Frontiers Research Topics

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Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by <i>in-situ</i> optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.

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Section: The Move Toward Automationmentioning

confidence: 99%

Deep Learning for Marine Science

2024

Frontiers Research Topics

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Section: The Move Toward Automationmentioning

confidence: 99%

Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS

et al. 2023

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Assessing the health of fish populations relies on determining the length of fish in sample species subsets, in conjunction with other key ecosystem markers; thereby, inferring overall health of communities. Despite attempts to use artificial intelligence (AI) to measure fish, most measurement remains a manual process, often necessitating fish being removed from the water. Overcoming this limitation and potentially harmful intervention by measuring fish without disturbance in their natural habitat would greatly enhance and expedite the process. Stereo baited remote underwater video systems (stereo-BRUVS) are widely used as a non-invasive, stressless method for manually counting and measuring fish in aquaculture, fisheries and conservation management. However, the application of deep learning (DL) to stereo-BRUVS image processing is showing encouraging progress towards replacing the manual and labour-intensive task of precisely locating the heads and tails of fish with computer-vision-based algorithms. Here, we present a generalised, semi-automated method for measuring the length of fish using DL with near-human accuracy for numerous species of fish. Additionally, we combine the DL method with a highly precise stereo-BRUVS calibration method, which uses calibration cubes to ensure precision within a few millimetres in calculated lengths. In a human versus DL comparison of accuracy, we show that, although DL commonly slightly over-estimates or under-estimates length, with enough repeated measurements, the two values average and converge to the same length, demonstrated by a Pearson correlation coefficient (r) of 0.99 for n=3954 measurement in ‘out-of-sample’ test data. We demonstrate, through the inclusion of visual examples of stereo-BRUVS scenes, the accuracy of this approach. The head-to-tail measurement method presented here builds on, and advances, previously published object detection for stereo-BRUVS. Furthermore, by replacing the manual process of four careful mouse clicks on the screen to precisely locate the head and tail of a fish in two images, with two fast clicks anywhere on that fish in those two images, a significant reduction in image processing and analysis time is expected. By reducing analysis times, more images can be processed; thereby, increasing the amount of data available for environmental reporting and decision making.

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