Use computer vision in any image based application and save time from human intervention.
Check for faulty items, large or big and be proactive in your processes.
Automate processes in retail, healthcare, manufacturing, transportation and other industries.
Use state-of-the-art image recognition features including people or object detection, image segmentation, keypoint detection, pose estimation, face recognition and analysis. Create applications that can actually understand the world and make educated decisions.
Object Detection is a computer vision technique to identify and locate objects in a video feed. It can be used to recognise and count objects and track their locations.
INCISIVE aims to create a pan-European platform of annotated cancer images for doctors and researchers in the field. The images will be annotated for cancer detection using state-of-the-art AI models and the most recent ethical practices.
Squaredev’s main role in the project is to provide an XAI (explainable AI) service so that doctors and researchers can understand how the models came to produce the outcomes they are reading.
The aim of LAW-GAME is to train police officers’ on the procedure, through gamification technologies in a safe and controlled virtual environment. Essential tasks during the creation of LAW-GAME game are to virtualise and accurately recreate the real world crime scenes.
One of the microservices we are providing, is an image annotation module to detect criminal items from crime scenes images. Another microservice we are building is a hostile pose detection service designed to detect a human that is trying to hurt another human.
DRYADS aims to build a holistic Fire Management platform. DRYADS project will make use of a real-time risk evaluation tool, and input coming from satellites and drones.
Our role in this project is to build an image classification application that will receive and classify forest images taken from drones so that first responders can react faster and classify to what extent a fire has hurt the forest.