Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS
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 remai...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-06-01
|
| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2023.1171625/full |
| _version_ | 1797813650626445312 |
|---|---|
| author | Daniel Marrable Sawitchaya Tippaya Kathryn Barker Euan Harvey Stacy L. Bierwagen Mathew Wyatt Scott Bainbridge Marcus Stowar |
| author_facet | Daniel Marrable Sawitchaya Tippaya Kathryn Barker Euan Harvey Stacy L. Bierwagen Mathew Wyatt Scott Bainbridge Marcus Stowar |
| author_sort | Daniel Marrable |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T07:55:55Z |
| format | Article |
| id | doaj.art-2f54b371efa044cc8cb3730a35c81a36 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-13T07:55:55Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-2f54b371efa044cc8cb3730a35c81a362023-06-02T05:29:21ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452023-06-011010.3389/fmars.2023.11716251171625Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVSDaniel Marrable0Sawitchaya Tippaya1Kathryn Barker2Euan Harvey3Stacy L. Bierwagen4Mathew Wyatt5Scott Bainbridge6Marcus Stowar7Curtin Institute for Computation, Curtin University, Perth, WA, AustraliaCurtin Institute for Computation, Curtin University, Perth, WA, AustraliaCurtin Institute for Computation, Curtin University, Perth, WA, AustraliaCurtin University, School of Molecular and Life Sciences, Perth, WA, AustraliaAustralian Institute of Marine Science, Townsville, QLD, AustraliaAustralian Institute of Marine Science, Indian Ocean Marine Research Centre, The University of Western Australia, Perth, WA, AustraliaAustralian Institute of Marine Science, Townsville, QLD, AustraliaAustralian Institute of Marine Science, Townsville, QLD, AustraliaAssessing 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.https://www.frontiersin.org/articles/10.3389/fmars.2023.1171625/fullstereo-BRUVSdeep learningautomated fish lengthphotogrammetrymachine learningcameras |
| spellingShingle | Daniel Marrable Sawitchaya Tippaya Kathryn Barker Euan Harvey Stacy L. Bierwagen Mathew Wyatt Scott Bainbridge Marcus Stowar Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS Frontiers in Marine Science stereo-BRUVS deep learning automated fish length photogrammetry machine learning cameras |
| title | Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS |
| title_full | Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS |
| title_fullStr | Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS |
| title_full_unstemmed | Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS |
| title_short | Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS |
| title_sort | generalised deep learning model for semi automated length measurement of fish in stereo bruvs |
| topic | stereo-BRUVS deep learning automated fish length photogrammetry machine learning cameras |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2023.1171625/full |
| work_keys_str_mv | AT danielmarrable generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT sawitchayatippaya generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT kathrynbarker generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT euanharvey generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT stacylbierwagen generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT mathewwyatt generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT scottbainbridge generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs AT marcusstowar generaliseddeeplearningmodelforsemiautomatedlengthmeasurementoffishinstereobruvs |