Registre des initiatives en IA

71 initiative(s) courante(s)

• 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

• 29

  AI-Enabled Detection of Watercraft for Aquatic Invasive Species Risk Management

  This 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

  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

• 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

• 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

• 50

  Salmon Data Digitization​ (See B22, B26)

  Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format so that it is accessible, editable, and useable for analysis. Document Intelligence - Custom Classification and Extraction Model Azure OpenAI GPT4o mini Data and Information from ~45,000 pages have been digitized

  Pilote Secteur des programmes Perception et compréhension IA multimodale

• 52

  "Chumputer Vision" (see B32) Salmon Scale Aging​

  A 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.

  Pilote Secteur des programmes Perception et compréhension Vision par ordinateur

• 58

  Phytoplankton image classification model

  Create a neural network model to classify phytoplankton images collected by in-situ phytoplankton imaging sensors. There are two discrete steps to this project: 1) create a library of annotated images for model training; 2) creation and refinement of the CNN model. Convolutional Neural Network, InceptionV3 and ResNet

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

• 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.

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

• 64

  Automatic detection of unidentified fish sounds

  We 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

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Détection d'événements sonores

• 66

  Optical Character Recognition Data transcription tool (See B6, B26)

  Built a proof of concept tool to facilitate data transcription and validation from hand-written field notes. Tested on ~100 year old salmon observations and present-day salmon stream inspection logs to determin ethe cost/feasibility/effectiveness of implementing such a tool in Salmon monitoring contexts. (Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format) Computer vision (azure document intelligence),

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

• 68

  Stereo camera image analysis

  Tools built in python to automate image analysis and fish measurement tasks. Applications include a conveyor belt camera and underwater towed stereo camera system Computer vision tools (Python, Viame)

  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

• 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

• 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

• 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