The chassis settings of a go-kart vehicle significantly affect the grip on the track. Each setting and each component on a go-kart affects the grip and driving in a different way. Although different combinations of settings can lead to similar results, the vehicle's behaviour on the track is different. Each driver has their own combination of settings that suits them best. While experienced drivers can easily recognize whether the chassis settings are appropriate and how to correct them in case of inadequacy through their feelings and observations while driving and the appearance of the tire treads, less experienced drivers have difficulty even in determining whether the settings are appropriate and whether, in case of inadequacy, the problem is too much or not enough grip. In this master's thesis, we developed a system for automatically recognizing the adequacy of the chassis settings of a go-kart vehicle based on images of tire treads. We designed the system based on convolutional neural networks and the semantic segmentation method. The results show the promise of using the semantic segmentation method.
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