Optimal Selection of the Workpiece Recognition Parameters by Minimizing the Total Error Cost

Abstract

Workpiece recognition is crucial in many flexible assembly cells, in automatic unmanned workstations and in robotic cells for human-robot collaborative scenarios. It allows for variability in the workpiece location & pose, and timing of the various operations. Automatic object recognition systems often rely on supervised machine learning methods, their parameters have to be chosen before training and cannot be changed later in runtime. In many machine learning applications the parameters are chosen in order to optimize the measures of relevance: accuracy, precision and recall. Usually these conflicting performance metrics are optimized independently from production costs. The innovation of present study is the introduction of a new metric to be optimized, a dimensionless total cost. It is a linear combination of the false positive and the false negative rates which are directly proportional to the real-life costs of errors. The presented case study, an object recognition system for reflective workpieces, is applied the proposed parameter selection to achieve optimal results with minimum production costs.