Computer Vision and Deep Learning
Computer Vision aims to allow computers to understand and interpret the visual information contained within images and videos.
The algorithms used in Computer Vision are constantly evolving. The use of Machine Learning and especially Deep Learning allowed to overstepping the limits of traditional Computer Vision techniques, so that artificial vision is increasingly used even outside factory automation.
Here are some examples of using computer vision algorithms.
Computer vision is used to improve the diagnosis of diseases such as tumors and atherosclerosis, to analyze the patient’s body movements and to measure the size and detect changes in the patient’s organs.
Through drones it is possible to acquire images of crops. The fusion of information from the images with data from other sources allows the farmer to decide on the interventions to be carried out.
Structural and functional inspections
Through drones it is possible to acquire images of the structures to be inspected. Artificial vision allows to automatically detect malfunctions in photovoltaic panels and helps to detect defects in buildings, infrastructures and other structures.