LABS
Quantifying brand exposure and sponsorship ROI using computer vision
Sponsorship is a highly effective form of marketing, but accurately calculating ROI is challenging. Sponsors are justified in demanding detailed analysis related to spend and returns.
LABS
Making use of prediction uncertainties in neural network outputs
We’re trying to figure out how we can use prediction uncertainties in deep learning network outputs to minimise false positives and prepare more targeted training data.

LABS
Transforming Environmental Management with AI
We use AI to better understand, manage and protect our natural world.

LABS
Transforming Biodiversity Management with AI
We developed a camera system and AI platform for detecting the presence of invasive plant species along linear infrastructure such as rail tracks and roads.


LABS
Detecting leaks and manufacturing imperfections
Keen AI developed simple computer vision based tools to highlight areas in a video feed where leaks are present.

LABS
Identifying Sand Dunes with AI
Shifting sands pose a threat to rail safety and overhead line clearances. We’re using satellite and ground based imagery to detect dunes and encroaching sand and then trying to model how it will move.

Keen AI Labs explore new use cases, technologies and approaches. Many work, others don’t, all are fascinating. Do you have a process that can be improved with AI? Learn more about our Labs process here, and our collaborators.

Partner Pending
active
Quantifying brand exposure and sponsorship ROI using computer vision
Synopsis:
Sponsorship is a highly effective form of marketing, but accurately calculating ROI is challenging. Sponsors are justified in demanding detailed analysis related to spend and returns. Is it time to consider how much brand exposure clients’ are actually getting?
Researchers:

Petar Gyurov

Amjad Karim
active
Monitoring sand dunes using satellite and train mounted camera data
Synopsis:
Shifting sands pose a threat to rail safety and overhead line clearances. We’re using satellite and ground based imagery to detect dunes and encroaching sand and then trying to model how it will move.
Researchers:

Dr. Ahmed Mahmoud

Amjad Karim

Petar Gyurov
Complete
Robots looking for bubbles to detect leaks and spot manufacturing imperfections
Synopsis:
Keen AI developed simple computer vision based tools to highlight areas in a video feed where leaks are present and present to a human who can repair the leak.
Outcomes:
Further Reading:
Researchers:

Hamzah Reta

Amjad Karim
Complete
Neural networks and camera systems for detecting invasive plant species
Synopsis:
We developed a camera system and AI platform for detecting the presence of invasive plant species along linear infrastructure such as rail tracks and roads.
Outcomes:
We trained models able to detect Himalayan Balsam and Ragwort from photographs taken from the car mounted system. Vehicle speed had to be kept under 30 mph for safety and for contiguous footage. Future work to focus on increasing speed to at least 50 mph.
Further Reading:
Researchers:

Hamzah Reta

Amjad Karim

Petar Gyurov

Dr. Tom August

ACTIVE
Bootstrapping computer vision model training using synthetic data
Synopsis:
Models require data to train on. Labelling this data manually is time consuming or the data may not exist. We’ve been working with a variety of techniques for generating synthetic data.
Outcomes:
Further Reading:
Researchers:

Thomas Emment

Hamzah Reta

Petar Gyurov

Amjad Karim
ACTIVE
Using model uncertainty to reduce manual labelling effort
Synopsis:
Researchers:

Qifan Fu

Prof. Greg Sablaugh

Petar Gyurov

Amjad Karim
Complete
Identifying ash trees at risk of ash dieback
Synopsis:
Following the learnings from our previous study on identifying invasive species from roadside images, and continuing our partnership with UKCEH, we set out to devise an effective operational pipeline for surveying the ash tree population and diagnosing ash dieback.
Outcomes:
Key insight was determining when in the year to collect footage. Ash tree seed pods are very distinctive and easier to detect in photos collected in late autumn.
Further Reading:
Researchers:

Petar Gyurov
