we design, build and optimise computer vision models that work with any hardware constraints
We rapidly build custom Computer Vision models trained against your unique data set, that you fully own
Do you have specific hardware needs or are you detecting something very niche and difficult? We can help
Deeper Vision solves computer vision problems with AI
AI computer vision systems typically require training on a vast collection of labelled images in order to function effectively. This training process allows the algorithms to learn the characteristics of different objects and scenes, and to develop a model that can be used to recognize and classify them.
After completing the training process, the system can then be utilized for the analysis of new images and videos. The AI algorithms will process the visual data and use the trained model to identify and classify objects and scenes in the images. This analysis can then be used by other systems or applications, such as facial recognition systems or image search engines.
AI computer vision works by using machine learning algorithms to analyse and understand the content of images and videos. It is trained on a large dataset of labelled images, and then uses this knowledge to identify and classify objects and scenes in new images and videos.
After we have built your solution we deploy it securely to the cloud and monitor its performance over time, providing you with the peace of mind of Deeper Insights handling the end-to-end production of your critical business computer vision product
How Deeper Vision uses AI to process documents
Our Deeper Vision pipeline
What our computer vision solution offers
Data Augmentation
In data poor environments we can synthesise datasets automatically, to create enough training data to train a computer vision model
End-to-End Computer Vision Pipeline
We provide complete automation from data ingest to model deployment to model monitoring in the cloud
Train Custom Models
Open source models can be custom trained on the clients data set by one of our AI Data Scientists
Hardware Configuration
Working with our clients on ideal data capture for best performance. Training models specific to the image size/type for optimal performance and reducing inference costs. Experiment with multiple/hardware (in-house and cloud-provided GPUs) addressing customers requirements.
Model Optimization
Downscaling model size to fit business requirements such as running costs, throughput, or hardware limitations
What you get: a trained computer vision model
We deliver your model via Jupyter notebook and python wheel that you can host and run on your own infrastructure
Or
A managed service where we host and run the trained model, providing ongoing support to monitor the models performance and provide re-training services. In line with EU AI Act regulations.
Deeper Vision acceleratES MANY computer vision USE CASES
Ocean rec
We detect protected species caught in the nets of The Ocean Cleanup to automatically alert the vessel to slow down or release the catch
Flight rec
On images acquired by drones, we detect light aircraft and helicopters at a distance of up to 1km
Real-time object detection and segmentation
Auto-registration of Femur and Tibia for markerless navigation within Smith & Nephew’s Assisted Robotic Surgery system
Animal rec
For a government agency, we identify and track protected animals to ensure the safety and security of the animals across a national footprint
Case study: Smith+Nephew use our AI to speed up navigation in robotic surgeries
This results in faster than real-time image segmentation of the human anatomy with 90%+ effectiveness
Project overview
As there is a lot of complex motion and granular detail, surgery image data is very complex to analyse. Our consortium, which is comprised of Smith&Nephew Ltd, Deeper Insights and Imperial College London, won Innovate-UK funding to tackle this problem.
We developed custom Computer Vision algorithms using NN's - Deep Learning to identify body parts in medical images. This leads to Markerless Navigation - the ability to detect where to cut bone on a knee for a knee replacement in Robotic surgery.
The challenge
In the UK, the total number of Total Knee Replacements (TKA's) per year has increased from 13,546 in 2003 to 98,147 in 2019 costing the NHS an estimated £585m per year. The average cost of a TKA in the UK is £12,000, however, post-surgical complications, e.g surgical site infection, increases this cost by between £1618 and £2398 per patient.
The solution
Our ambitious and innovative project focussed on developing markerless and automated registration to track the patient's limbs. This was tailored for robotic-assisted orthopedic procedures using structured light technology assisted by deep learning to continuously capture the patient's anatomy during surgery.
The end result
This new platform will be integrated within S&N's commercially available robotic platform 'NAVIO', which was previously supported by I-UK funding, and will obviate the need for percutaneous markers reducing set-up time, cost and complexity during surgery.
This results in faster than real-time image segmentation, with above 90% accuracy, of the human anatomy.
A deeply trusted, expert partner
It's never been done before and will change the category."
Smith & Nephew
GSMA
Computer vision insights from OUR BLOG
Simplifying Chest X-ray Diagnosis with AI
Exploring the Future of Multi-Modal Embeddings with ImageBind
The AI Revolution in Skincare
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