On human perception and automatic target recognition: Strategies for human-computer cooperation

This work addresses the task of underwater object recognition in sonar imagery when both human operators and automated algorithms are available. We discuss the issues that have impeded previous attempts at automation, raise key insights related to human perception, present strategies to exploit the skills of humans and computers synergistically, and demonstrate the utility of the proposed approaches on a real object-recognition task employing actual humans acting as operators. Importantly, the strategies outlined here can be immediately adopted in existing (unautomated) target recognition systems with minimal cost, effort, and risk, while still achieving potentially significant performance gains. Moreover, this progress lays the foundation for the acceptance of still-further automated systems in the future. Experimental results are provided from a real mine-search exercise at sea, with recognition performance as a function of human operator effort given for various human-computer divisions of labor.