The degree project focused on automating industrial processes in heavy truck manufacturing using machine learning (ML) and computer vision. Specifically, it addressed the challenge at the Volvo Trucks plant in Tuve, Gothenburg, of accurately measuring the height of finished trucks. Manual measurement methods that were in use were prone to errors and inefficiencies, prompting the need for an automated solution implemented through the Volvo Vision System (VVS).

The project developed and evaluated a proof-of-concept (POC) system that could be integrated into VVS for automated truck height measurements. The system incorporated camera calibration, ML models, and computer vision techniques. Testing on various truck models and configurations demonstrated that the system provided reliable detection of trucks cabs, variations of addons that were random and variable in geometry and perform measurements within an acceptable error margin 90% of the time, with a mean deviation of 2.25 cm and a standard deviation of 2.36 cm. The automated system significantly outperformed manual methods, reducing the measurement time per truck from 60 seconds to 120 milliseconds.

The study concluded that the enhanced VVS could reliably measure truck heights within the required industrial error margins. The automation of the measurement process not only increased accuracy but also improved efficiency, aligning with Volvo’s goal of reducing man-ual interventions.

The project highlighted the potential of integrating AI and computer vision in industrial applications, paving the way for future enhancements and broader applications in similar settings. Future research could focus on expanding the system's capabilities and adapting it to other industrial environments.