Grant: Alliance Grants
Memorial University of Newfoundland
$26,165.00
OFI SF5 Advanced capacity for autonomous detection of green sea urchin (Strongylocentrotus droebachiensis)
EF 2020-21
Résumé
Over the last 5 years, Dr. Gagnon and his industry partners have shown that a Newfoundland green sea urchin (Strongylocentrotus droebachiensis) gonad industry is both achievable and highly desirable. There is a pressing need to develop new technologies to quickly and efficiently collect green sea urchins, and more information is needed to sustainably manage sea urchin stocks and their habitats. Remotely operated vehicles (ROV) offer this potential, yet there is currently no commercially available ROV solution for this specific purpose. To capitalize on momentum created by Dr. Gagnons previous research, the proposed OFI Seed Fund project aims to develop the foundation of advanced ROV capacity for intelligent, semi-autonomous recognition and collection of urchins under natural environmental conditions and habitat characteristics. This multidisciplinary project will build sea urchin identification capacity based on image recognition and machine learning. It will be addressed by developing an integrated suite of computer programs that can recognize and electronically tag urchins on videos of seabed acquired with standard and 3-D submersible HD and laser camera systems, and progressively improve at urchin recognition via self-learning capacity. The project will be assigned to a top-notch PhD candidate with relevant background in Artificial Neural Networks and Computer Vision. It will act as a springboard enabling three researchers from different disciplines at Memorial University and their colleagues from NRC and Kraken Robotics (industry partner) to collaborate on this new initiative that presents a strong potential for patenting/commercialization and eventual inclusion of other marine species.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Newfoundland and Labrador, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
559820-2020
Name nom
Gagnon, Patrick
Department département
Ocean Sciences
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
26165
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
4700
Research subject group en
Evolution and ecology
Research subject group fr
Évolution et écologie
Research subject en
Evolution and ecology
Research subject fr
Évolution et écologie
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
OFI SF5 Advanced capacity for autonomous detection of green sea urchin (Strongylocentrotus droebachiensis)
Keyword
NSERC
Application summary
Over the last 5 years, Dr. Gagnon and his industry partners have shown that a Newfoundland green sea urchin (Strongylocentrotus droebachiensis) gonad industry is both achievable and highly desirable. There is a pressing need to develop new technologies to quickly and efficiently collect green sea urchins, and more information is needed to sustainably manage sea urchin stocks and their habitats. Remotely operated vehicles (ROV) offer this potential, yet there is currently no commercially available ROV solution for this specific purpose. To capitalize on momentum created by Dr. Gagnons previous research, the proposed OFI Seed Fund project aims to develop the foundation of advanced ROV capacity for intelligent, semi-autonomous recognition and collection of urchins under natural environmental conditions and habitat characteristics. This multidisciplinary project will build sea urchin identification capacity based on image recognition and machine learning. It will be addressed by developing an integrated suite of computer programs that can recognize and electronically tag urchins on videos of seabed acquired with standard and 3-D submersible HD and laser camera systems, and progressively improve at urchin recognition via self-learning capacity. The project will be assigned to a top-notch PhD candidate with relevant background in Artificial Neural Networks and Computer Vision. It will act as a springboard enabling three researchers from different disciplines at Memorial University and their colleagues from NRC and Kraken Robotics (industry partner) to collaborate on this new initiative that presents a strong potential for patenting/commercialization and eventual inclusion of other marine species.