Press release: Antmicro to develop 3D vision system for X-MINE mining project
Published:
Antmicro, a globally operating company specialized in early adoptions of latest vision technologies in the embedded area, has announced its participation in the €12 million X-MINE project, targeting the mining and raw materials industry.
The project, which officially started on June 1st, 2017 with a timeframe of 3 years, will be executed by a Consortium of 5 industrial companies, 4 research/academic organizations, 4 mining companies and 1 mining association. With technology partners and mining stakeholders onboard, the Consortium’s goal is to develop a state-of-the-art system to support Real-Time Mineral X-Ray Analysis for Efficient and Sustainable Mining set to address Europe’s growing challenges in access to and efficient processing of Critical Raw Materials.
The initiative’s overall budget tops €12 million, with the aim to “support better resource characterization and estimation as well as more efficient ore extraction in existing mine operations, making the mining of smaller and complex deposits economically feasible and increasing potential European mineral resources (specifically in the context of critical raw materials) without generating adverse environmental impact.”
Antmicro will be creating an AI-supported 3D vision system to facilitate accurate ore segregation on-site, to be deployed for testing in 3 operating European mines - in Sweden, Greece and Bulgaria.
Building on unique experience with latest parallel processing FPGA and GPU technologies, most notably Xilinx UltraScale+, and the NVIDIA Jetson family of embedded CPU+GPU SoCs, Antmicro aims to successfully employ advanced 3D vision and tracking solutions in a system which also uses X-ray scanning for identifying valuable materials.
Antmicro’s participation in X-MINE brings necessary software and hardware expertise on high-speed signal processing in latest vision platforms and promises an end solution that will be offered to stakeholders in the mining and raw materials industry looking to introduce efficient image recognition systems in their fields.
