Reinforcing conceptual engineering design with a hybrid computer vision, machine learning and knowledge based system framework

We propose a novel system that aids engineers in the conceptual stage of design. Our system´s goal is to support the engineer without limiting his creative role; thus, our proposed method does not produce ready study solutions but rather actively monitors the design procedure, verifying design stages and pointing out potential mistakes. This is achieved with a hybrid computer vision, machine learning and knowledge based system framework. Design stage identification is performed with a novel algorithm which comprises a classification stage based on Random Forests and examination of the temporal relationships between the engineer´s actions with the aid of statistical graphical models. Experimental results captured in a complex, real life scenario demonstrate our system´s ability to efficiently support the engineer´s decisions during the conceptual stage of design.