Underwater Computer Vision Course
This course prepares you to apply computer vision to images and video captured underwater, where light is absorbed, scattered and shifted in color. You will learn to correct and enhance underwater images, train deep learning models to detect and classify species, objects and damage, reconstruct the seabed in 3D with photogrammetry, and combine cameras with sonar on robots and observation platforms.
Prior knowledge check
These are the skills this program gives you. Tick the ones you already master and we will tell you which part of the syllabus is for you.
You are starting from scratch in this subject. The program is designed precisely for that.
What is the Underwater Computer Vision Course?
This short course prepares you to teach machines to see underwater. You will learn how absorption and scattering shape underwater images, how cameras, housings, lighting and calibration are handled below the surface, how color correction, dehazing, contrast enhancement and noise reduction improve image quality, how convolutional neural networks are trained for detection and segmentation with labeled datasets, data augmentation and transfer learning, and how stereo vision, photogrammetry, mosaicking and camera-sonar fusion support 3D reconstruction and robot navigation.
What sets this course apart: a focus on the specific challenges of turbidity, light absorption and scarce labeled data; work with open tools such as Python, OpenCV and deep learning libraries; varied applications in biodiversity monitoring, inspection and robot navigation; coverage of underwater 3D photogrammetry and seabed reconstruction; an agile format of five application-focused modules; and a practical case in which you develop a detector using real underwater images.
Program highlights
- Specific challenges: turbidity, light absorption and scarce labeled data.
- Open tools: Python, OpenCV and deep learning libraries.
- Varied applications: biodiversity, inspection and robot navigation.
- Underwater 3D: photogrammetry and seabed reconstruction.
- Sensor fusion: combining cameras and sonar for perception.
- Practical case: developing a detector with real underwater images.
Syllabus of the Underwater Computer Vision Course
5 modules. Select a module to see its content.
Underwater imaging and optics
- 1.1. Absorption and scattering of light
- 1.2. Underwater cameras and housings
- 1.3. Artificial lighting
- 1.4. Underwater camera calibration
- 1.5. Data formats and volumes
Image processing and enhancement
- 2.1. Color correction
- 2.2. Dehazing
- 2.3. Contrast enhancement
- 2.4. Noise reduction
- 2.5. Image quality assessment
Deep learning for underwater images
- 3.1. Convolutional neural networks
- 3.2. Object detection
- 3.3. Image segmentation
- 3.4. Datasets and labeling
- 3.5. Data augmentation and transfer learning
3D reconstruction and perception
- 4.1. Stereo vision
- 4.2. Underwater photogrammetry
- 4.3. Seabed mosaics
- 4.4. Camera and sonar fusion
- 4.5. Vision for robot navigation
Applications and deployment
- 5.1. Biodiversity monitoring
- 5.2. Inspection of structures and hulls
- 5.3. Marine litter detection
- 5.4. On-board and real-time processing
- 5.5. Practical case: detector with real images
Duration and format of the Underwater Computer Vision Course
It is 300 hours. Tell us how many you can study each week and we will tell you when you will really finish.
This is the pace at which most students finish. It fits well with the installment plan.
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programs.
Dr. Marina AlcoverAcademic Director and Head of Quality Assurance
Eng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints
Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator
Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory
Dr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator
Eng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator
Eng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line
Eng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration CoordinatorObjectives of the Underwater Computer Vision Course
Career opportunities after the Underwater Computer Vision Course
Questions about the Underwater Computer Vision Course
Yes, a basic level of Python is recommended to follow the exercises.
No. You can use cloud computing environments with free or low-cost resources.
Yes. You work with public datasets of underwater images.
Acoustic imaging and its fusion with cameras are introduced, although the focus is on optical imaging.
You develop and evaluate a detector using real underwater images.
Fully online, through a virtual campus available 24/7, with live sessions that are recorded and tutorials with the teaching staff.
The course comprises 300 hours of study across 5 modules of about 60 hours each and ends with an applied practical case. You can progress at your own pace within the schedule set when you enroll.
More programs in Underwater imaging
Admission process
No waiting for a call back. If your documents are in order, you can enroll in the same session.
- Profile: aimed at computer, telecommunications and electronic engineers who want to apply computer vision to the sea, data scientists interested in underwater imaging, marine biologists who analyze large volumes of video, underwater inspection technicians working with ROVs and cameras, and marine robotics developers who need visual perception; prior studies or professional experience in engineering, computing, data science, marine sciences or underwater inspection are recommended.
- Language: sufficient command of English to follow the classes and the technical documentation.
- Technical resources: a computer with a stable internet connection, an up-to-date browser and a camera for live sessions and project presentations.
- Admission: online application, review of your profile and a short interview with the academic team.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrollmentChoose full payment or instalments and reserve your place.
Admission is provisional until the committee validates the documents provided. If you do not meet the requirements, the full amount is refunded.
Ask us before you enroll
Leave your details and admissions will write to you to answer any questions. If you are already sure, you can apply and pay in the same session.