When students at TalTech\u2019s Tartu College<\/a> were asked whether they\u2019d like to invent a machine that could detect knitting defects, none of them could have imagined it would become a nine-month journey leading to a real industrial solution. Today, the students have developed a machine vision-based quality control system capable of analysing knitted products directly on the production line and automatically highlighting any defects.<\/p>\r\n In collaboration with the Centre for Artificial Intelligence and Robotics (AIRE<\/a>) and the textile company Vikan AS, the students were tasked with automating the detection of knitting faults. It was a complex challenge, but the team \u2013 despite lacking prior professional experience \u2013 proved that with enough determination and a willingness to learn, a student project can evolve into a genuine industrial innovation.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/article><\/div>\r\nRandom start<\/h4>\r\nIt all started modestly, when lecturer Ago Roots<\/a> posed a question in the hallway that would transform the team\u2019s entire learning experience: \u201cWould you be interested in building a machine for a company?\u201d<\/em> The answer was a resounding yes, but the first setback followed quickly \u2013 the idea initially failed to secure funding. Some time later, a new opportunity arose through the AIRE programme, and the team seized it, eventually receiving support that allowed them to move forward with the project.\r\n
![\"Meeskonna](\"https:\/\/trialoog.taltech.ee\/wp-content\/uploads\/2025\/04\/IMG_3221-1-scaled.jpg\")
<\/a> The team\u2019s machine learning specialist Gregor Kokk focused on model development.