PROPHET: PReference-Based OPtimization for Human-cEnTric Visual Inspection

Nowadays, complex inspection processes rely heavily on human operators, while automatic systems handle simpler tasks. However, these tasks are highly repetitive and demand consistent high-quality performance throughout, leading to significant stress for human workers. In contrast, automatic systems can help alleviate this burden. Nevertheless, configuring automatic inspection systems is challenging due to numerous parameters that require extensive time and trial-and-error adjustments. To address these issues, this project aims to introduce an optimization approach based on user preferences for configuring visual inspection systems. Preference-based optimization is a potent method for enhancing system performance in an intuitive manner. This methodology enables the resolution of optimization problems when the decision-maker cannot directly assess the objective function tied to the problem at hand. Instead, they can only express preferences, such as “this option is better than that" when comparing different decision choices.