System for automatic size and type control of cars rims

At the end of every automotive assembly line, there is a quality control process where factory workers check produced cars for potential defects. The computer vision field, especially neural networks for images, have great potential to complement human staff in order to produce as safe and reliable cars as possible. In this thesis we focus on the validation, whether all four wheels on a single car match in size and type. We introduce and experiment with both neural networks and traditional computer vision techniques. The approach we use is to first detect the car then classify its wheels and try to estimate their size. In the end we build a functional prototype of the system that is running in real-time. The data for this thesis were recorded in Škoda Auto factory in Mladá Boleslav in cooperation with the company.