Robots operating in unstructured environments must cope with uncertain, unpredictable and dynamic situations, resulting in lack of information. Introducing a Human-Operator into the system can help simplify the robotic system and improve performance. In previous work we presented a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. The methodology is based on an objective function for target recognition in human-robot systems. In this paper the objective function was expanded to include operational and time costs that are both important for evaluating and optimizing system performance. Numerical analyses of the developed objective function, combined with signal detection theory, were applied for the predefined collaboration levels. Results indicate that optimal collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector.