Registre des initiatives en IA

71 initiative(s) courante(s)

• 87

  ANMPA Habitat Mapping Project

  Drop 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 49

  Salmon Fence Counting​

  A model using computer vision to analyze water stream fence video-footage, to simultanously count the number of and predict the species of salmon.​ YOLO 11 Chinook trained on 735 annotated frames from 63 videos Coho trained on 432 annotated frames from 44 videos Sockeye trained on 1149 annotated frames from 36 videos Chum untrained (annotation of 1368 frames from 43 videos in progress) Pink Humpback

  Pilote Secteur des programmes Perception et compréhension Vision par ordinateur

• 23

  Video Summarization for Aerial Surveillance

  AI pilot under development to automatically detect and flag relevant events (e.g., human/vessel activity) in surveillance video for information management. Action plan for this initiative: 1. Governance: Oversight by a multidisciplinary group, routine updates and risk assessments to DM. 2. Guardrails: Pilot with departmental data only, audit outputs for accuracy and bias. 3. Work processes: Deliver targette

  Pilote Secteur des programmes Perception et compréhension Vision par ordinateur

• 28

  AI-Enabled Electronic Monitoring (EM) Solutions

  The 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

  Pilote Politiques stratégiques Perception et compréhension Fusion de capteurs

• 33

  Machine-learning-assisted Benthic Marine Life Annotation Tool

  The goal of the project is to improve the efficiency of the image annotation process to reduce manual efforts of the benthic science team and reduce time-to-insight from underwater surveys. Further development is prevented by DFO's IT infrastructure. There are ongoing discussions with CDOS to resolved this but the project is on hold in the meantime. Client: Quebec Region/Pacific Region - Oceans and Ecosystem Sciences Division

  Idéation En pause Politiques stratégiques Perception et compréhension Vision par ordinateur

• 34

  AI-assisted Marine Mammal Annotation Tool

  The 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

  Pilote Politiques stratégiques Perception et compréhension Vision par ordinateur

• 35

  AI-assisted Solution to Detect Ghost Gear from Side Scan Sonar Images

  The goal of the project is use AI to automate data analysis in detecting Ghost Gear. Client: Ghost Gear Program

  Idéation En pause Politiques stratégiques Perception et compréhension Vision par ordinateur

• 36

  AI-assisted Solution to summarize C&P aerial surveillance videos

  A 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.

  Pilote Politiques stratégiques Perception et compréhension Vision par ordinateur

• 32

  CTD Anomaly Detection

  The 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

  Pilote Politiques stratégiques Perception et compréhension Analytique prédictive

• 74

  Pacific Plankton Monitoring: Imagery and automated classification

  • Monitoring of zooplankton and phytoplankton in the water column with shipboard and benchtop plankton imaging instruments (UVP, ZooScan, PlanktoScope). • Application of automated classification of survey and preserved sample image data sets to complement and enhance spatial and temporal resolution of monitoring in Canada’s EEZ and offshore NE Pacific. Development of and regular optimization of automated image classification models using AI: deep learning (residual networks) and machine learning (random forests) methods.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 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

  Idéation Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 81

  Topaz AI Software

  Enhance underwater video from ROV and tow camera deployments in the offshore region and elsewhere. The deployment of these platforms is incredibly time- and resource-consuming, making the optimization of available video an important part of getting the most from this imagery. In addition there are camera limitations from the available DFO survey tools and framegrabs from video will be used as a major product from surveys, making enhancement to high quality images crucial. Topaz Video AI and Topaz Photo AI

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 86

  Automatic detection of Arctic whales in aerial images

  In 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 72

  Extracting impacts data from regulatory documents

  Use OCR pdf readers, topic detection and language models to detect specific data elements inside written text documents, for extraction and analysis in a cumulative impacts context. Large lanugage models and topic clustering tools

  Idéation Sciences des écosystèmes et des océans Perception et compréhension Compréhension du langage naturel

• 88

  Development of an AI matching model for bowhead whale photo-identification

  Using DFO’s existing database of bowhead whale photographs, the specific objectives are: 1) Create a machine learning model “detector” for bowhead whales. 2) Create a machine learning individual re-ID model for bowhead whales to re-identify bowheads. 3) To make the detector and matching models accessible through an easy to use, freely available, web-based application, Flukebook.org.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 89

  Augmenting Whale Detection in Satellite Images using synthetic data

  An 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 90

  Deep Learning Enhances Beluga Whale Research in the Arctic

  Esri 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 91

  Localization and Tracking of Beluga Whales in Aerial video Using Deep Learning

  Deep-learning model was developed us YOLOv7 to detect beluga whales from imagery footage. The main contribution of this research is providing a system that accurately detects and tracks features of beluga whales, both adults and calves, from aerial footage.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 94

  DFO Bot, deep learning application to analyse otolith images available in DFO Dots

  Development of a neural network trained using otolith images, expert-derived age estimates, otolith annotations and annuli to obtain age estimates from otoliths.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 95

  Optimization of Machine Learning modelling strategy for coastal zooplankton taxonomic identification from flow microscopy imaging.

  As part of Aquaculture Monitoring Program we are using Ecotaxa's ML algorithm to identify zooplankton. The project aims at identifying the best strategy in building model training sets for each region monitored. This strategy will be the basis for future AMP zooplankton identification work.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 96

  Use of machine-learning (ML) algorithms and high-performance computing (HPC) for the optimization of satellite image analysis for mapping coastal habitats

  This project aims to improve satellite image analysis of marine habitats leveraging NRC HPC and ML assets to apply cutting-edge ML techniques to the challenges of remote sensing data analysis. Relates to ongoing project (below) through MPC service-level agreement.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 97

  Satellite image analysis for mapping eelgrass habitat in the southern Gulf of St. Lawrence.

  This project supports Gulf Region needs for spatial data representing eelgrass habitat through a service level agreement with Marine Planning and Conservation. Machine learning algorithms have been applied for classification of satellite imagery. MPC and Science have been collaborating for several years, annually purchasing satellite imagery, with classification conducted by both groups.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 100

  Development of AI technology for otolith and scale ageing

  Ageing of scales and otoliths is time-consuming, relies on skilled personnel, and often faces delays. AI tools like DFOdots offer a solution by enabling automated ageing using annotated reference collections, helping reduce backlogs and support training. Developing these AI models can enhance resilience, efficiency, and training capacity in ageing programs.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 103

  Improved understanding of the processes impacting harmful algal blooms

  Using machine learning algorithms to analyze phytoplankton images from the Imaging Flow Cytobot (IFCB) to recognize and quantify species associated with harmful algal blooms.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 104

  Zooplankton classification from Video Plankton Recorder

  Using machine learning algorithms to identify plankton for rapid zooplankton classification from the Video Plankton Recorder, in support of efforts to assess North Atlantic Right Whale foraging habitat.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur