Registre des initiatives en IA
Toutes les initiatives inscrites au registre sont consultables ici, sans compte. Chaque fiche montre son avancement, ses jalons et l'historique de ses décisions. Les demandes encore en cours de triage n'y figurent pas : elles rejoignent le registre une fois examinées.
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82 initiative(s)
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52
"Chumputer Vision" (see B32) Salmon Scale AgingA model using computer vision to look at images of salmon scale and predict the age of salmon and also monitor its ring growth. CNN and YOLO 9. Currently trained on 1478 images.
Pilot Secteur des programmes Perception et compréhension
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174
AI model for Video reviewAn AI model that reviews video monitoring footage for a fishery or given vessel and machine learns potential events of non compliance
Idea Secteur des programmes Perception et compréhension
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34
AI-assisted Marine Mammal Annotation ToolThe goal of the project is to improve the efficiency of the aerial image annotation process to reduce manual efforts of the marine mammal science team and reduce time-to-insight for marine mammal survey flights. This project is currently in progress. Business requirements have been finalized and development has commenced Client: Ecosystems and Oceans Science - Ecosystems Science Directorate
Pilot Politiques stratégiques Perception et compréhension
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35
AI-assisted Solution to Detect Ghost Gear from Side Scan Sonar ImagesThe goal of the project is use AI to automate data analysis in detecting Ghost Gear. Client: Ghost Gear Program
On hold En pause Politiques stratégiques Perception et compréhension
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36
AI-assisted Solution to summarize C&P aerial surveillance videosA pilot project to explore the use of AI/ML for automating the identification of relevant segments in EM video and imagery, such as human activity or vessel registration numbers (VRNs). This initiative aims to reduce manual review time and enhance the efficiency of information management in support of fisheries oversight.
Pilot Politiques stratégiques Perception et compréhension
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29
AI-Enabled Detection of Watercraft for Aquatic Invasive Species Risk ManagementThis pilot project, in collabration with Aquatic Invasive Species Program, develops an AI-powered system to automatically detect and track watercraft at key entry points to prevent the spread of Aquatic Invasive Species (AIS). By enabling early identification of vessels that may carry invasive organisms, the system supports faster inspections and intervention to protect aquatic ecosystems. Initial exploration using sample data is underway, with the project pending ADM approval for installing cameras and data collection. Client: Aquatic Invasive Species Program
Pilot Politiques stratégiques Perception et compréhension
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28
AI-Enabled Electronic Monitoring (EM) SolutionsThe pilot project, in collabration with Fisheries Resources Management, explores the integration of AI-enabled electronic monitoring (EM) solutions to automate the detection and classification of fishing activities using video and sensor data from vessels. This initiative aims to enhance compliance monitoring, reduce manual review time, and support evidence-based fisheries management. Client: Fisheries Resources Management
Pilot Politiques stratégiques Perception et compréhension
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165
AIMMS (Artificial Intelligence for Marine Mammals in Survey imagery)The AIMMS project develops a platform to automate and accelerate the interpretation of aerial imagery for marine mammal population assessments. By streamlining annotation and review, and enabling scientists to develop custom machine-learning models without advanced coding skills, the AIMMS workflow significantly reduce processing time, accelerate delivery of science advice, and lower operational costs.
Proof of Concept Gestion des pêches Perception et compréhension
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87
ANMPA Habitat Mapping ProjectDrop camera survey to document seafloor habitats and biological communities in the Anguniaqvia niqiqyuam Marine Protected Area (ANMPA) in the Inuvialuit Settlement Region. A project sub-objective is to develop AI capacity to detect and record bottom habitat classifications and benthic animals from video still-images, substantially reducing processing times.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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187
Atlantic Bluefin Tuna MonitoringAutomatically perform video analytics from hundred of harvester tuna video Solution being developed in region to assist C&P in batch processing of at-sea video monitoring footage.
Pilot Affaires autochtones, aquaculture et gouvernance Perception et compréhension
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89
Augmenting Whale Detection in Satellite Images using synthetic dataAn autodetector model was built to detect whales in satellite imagery using synthetic imagery. The primary author is now testing the algorithm on satellite imagery obtained by Arctic Region that has been manually read for whales.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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126
Automated assessment of satellite imageryAI routines are being developed and tested to assess large volume of satellite imagery and data to: i) Automatically Identify marine hazards and ii) Change detection – i.e. shorelines or riverbank changes.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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223
Automated assessment of satellite imageryAI routines are being developed and tested to assess large volume of satellite imagery and data to: i) Automatically Identify marine hazards and ii) Change detection – i.e. shorelines or riverbank changes.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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86
Automatic detection of Arctic whales in aerial imagesIn collaboration with Dr. David Clausi and his team at the Vision and Image Processing (VIP) Research Lab (University of Waterloo), we have been developing an algorithm to detect narwhals and belugas in photos from aerial surveys. We are planning to expand to ice seals and walrus.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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64
Automatic detection of unidentified fish soundsWe used Random Forestes and Convolutional Neural Networks (CNN) algorithms trained on manually detected fish sounds to detect fish sounds collected on a passive acoustic monitoring project. The result is an easy-to-use, open-source software called FishSoundFinder, implemented with the CNN detector. Random Forests and CNN
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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145
Automatic detection of unidentified fish soundsThis completed project used Random Forests and Convolutional Neural Networks (CNN) algorithms trained on manually detected fish sounds to detect fish sounds collected on a passive acoustic monitoring project. The result is an easy-to-use, open-source software called FishSoundFinder, implemented with the CNN detector.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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159
Chumputer Vision - salmon scale age predictive AIDeep Machine growth pattern Learning of chum salmon scales for predictive age assignments by way of scale pattern interpretation.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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76
Chumputer Vision - salmon scale age predictive AI (See B8)Deep Machine Learning and Convolutional Neural Network application for the predictive application of age assignment of Chum salmon by way of scale images. Chum salmon has been selected as the gateway species due to its relatively easy scale pattern interpretation, this will foster the pathway forward for Chinook, Coho Sockeye and Herring as future targets for AI age interpretation. Deep Machine Learning and Convolutional Neural Network within a Python predictive algorithm
Idea Sciences des écosystèmes et des océans Perception et compréhension
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188
Coastal SurveillanceAutomatically process thousands of drone images as part of continuous surveillance Solution being developed in region to assist C&P in batch processing of drone surveillance images.
Pilot Affaires autochtones, aquaculture et gouvernance Perception et compréhension
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105
Combining artificial intelligence and drone imagery for automatic extraction of morphometric attributes of small cetaceans: Application to the St. Lawrence Estuary Beluga population.This project proposes to use a multiyear time series of drone images of the St. Lawrence Estuary Beluga population to develop a series of algorithms for automatic extraction of morphometric information of individual beluga whales using AI.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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119
Computer vision to automate fish data extraction from videoOur purpose has been to build a tool to broadly apply computer vision methods in marine research. Our work has generated several publications illustrating how we built, tested/validated, and applied computer vision methodology to collect / extract / and analyze data pertaining to marine fish in the Newfoundland and Labrador region, from video. For example, we have used it to examine the impact of seismic surveying on commercial fish in the Newfoundland and Labrador offshore. Our continuing purpose is now broadly sharing our system with commercial harvesters (fishermen), non-profit organization (AHOI), government researchers (aquaculture, MPAs, AIS), and academics (computing scientists), to collect and rapidly analyzing video data for a wide range of applications.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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32
CTD Anomaly DetectionThe goal of this project is to apply machine learning to detect anomalous oceanographic data, highlighting meaningful physical phenomena in the state of the ocean for closer investigation by scientists. A proof of conept model has been developed to demonstrate feasibility. Further validation is required with the client to verify alignment with the needs of scientists. Client: Pacific Region - Ocean Sciences Division
Pilot Politiques stratégiques Perception et compréhension
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169
Dark Vessel Detection PlatformUtilizes AI and machine learning to detect vessels in satellite imagery, predict movements, and flag potential IUU fishing globally.Automatically cross-references detected vessels with AIS and VMS data to identify unregistered or "dark" vessels. Supports international IUU fishing monitoring; new AI-driven features enable vessel identification from electro-optical imagery. Governance: Managed by DFO’s International Fisheries Enforcement team; technical oversight by MDA Space; coordinated with CSA and other OGDs. Guardrails: All data use complies with commercial licensing terms and GC privacy requirements; sharing restricted
Pilot Secteur des programmes Perception et compréhension
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90
Deep Learning Enhances Beluga Whale Research in the ArcticEsri Canada built a deep learning model that automatically detects beluga whales in imagery. The model was developed using satellite, drone and aerial imagery and reported accuracy is high, but the model has not been tested by DFO Science yet.
Pilot Sciences des écosystèmes et des océans Perception et compréhension
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62
Deep sea habitat, organism and object detection and identification.Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the results into Biigle for human review. TBD.
Idea Sciences des écosystèmes et des océans Perception et compréhension