IFAE’s spin-off Deep Detection has secured the investment from specialist photonics VC Vigo Ventures, Clave Capital, ESOM, and public financing. Founded in July 2020, Deep Detection is preparing to launch its first product, PhotonAI, an X-ray camera with deep learning capabilities. The backbone of the technology used by Deep Detection is the know-how obtained by the BIST centre IFAE from having invented and developed novel instruments to explore fundamental science and address technological challenges.
Deep Detection, the spin-off from the Institut de Física d’Altes Energies (IFAE), a BIST centre, launched by The Collider – Mobile World Capital Barcelona, has received a 1M€ pre-seed investment round mainly contributed by two venture capital companies: Vigo Ventures (Poland), specialized in disruptive photonics technologies, and Clave Capital, represented by Tech Transfer Agrifood (Spain) and focused on agrifood chain innovative solutions. Energy Services Operation and Maintenance (Spain) has also participated as a private investor. The spin-off also received public contributions from the regional and the national competitive funds, Startup Capital (ACCiÓ), and Torres Quevedo (MICINN).
Deep Detection was founded in 2020 by a scientific team including IFAE researchers Prof. Mokhtar Chmeissani and Dr. José Gabriel Macías, and a business team formed by David Ciudad (CEO) and Colin Burnham (COO). In less than a year the company has been positioned as one of the most relevant deep tech projects with application in the food industry in the national ecosystem, with granted patents in the United States, Europe, Japan, and China. The project has achieved several recognitions, such as the ‘Siemens Award’ for the best solution in automation and robotics, granted within the framework of ‘Food 4 Future ExpoFoodTech’, and the ‘Santander X Spain Award 2021’, granted by Banco de Santander.
Deep Detection will allocate the pre-seed resources in the production of the first pilots for a novel high spectral resolution x-ray camera. The device will enable the current industrial food inspection processes to be performed at high speed, thanks to a patented technology developed by IFAE. The technology is based on a specific read-out chip and a photon counting algorithm that provides the ability to detect low density foreign bodies in food, an actual challenge that the food industry is trying to solve. The world food market suffers losses, due to product defects, worth 5B$ annually because of the limited performance of the current inspection systems
Deep Detection’s next development phase is evaluating an Industrial X-ray line scan camera in collaboration with customers and end-users. Deep Detection’s aim is to introduce its first multispectral camera to market by 2022 and a family of X-ray line scan cameras will follow shortly.
IFAE technology for Spectral X-ray Pixel detector
The backbone of the technology used by Deep Detection SL is the know-how obtained by IFAE from having invented and developed novel instruments to explore fundamental science but also to address technological challenges. One example is IFAE’s knowledge of state-of-the-art pixel X-ray detectors, which allowed it to obtain a patent in X-ray photon counting with spectral response.
While the current applications of Deep Detection’s technology are addressed to the food industry, their scope can be extended to Non-Destructive-Tests (NDT) to ensure highly reliable product quality control. Furthermore, the use of this technology can be extended to the field of medical imaging, in particular Computed Tomography (CT), yielding a novel imaging modality based on detecting signals in several fine energy bins.
This technology has been developed at IFAE and is a very powerful tool when blended with Artificial Intelligence. It is a vivid example of how investing in fundamental research improves the well-being and quality of life of people, creates business opportunities, and generates highly-qualified jobs in sectors such as microelectronics design, image processing, artificial intelligence, and machine learning.
Learn more on the IFAE website.