Multi-Objective Genetic Programming for object detection

In object detection, the goals of successfully discriminating between different kinds of objects (object classification) and accurately identifying the positions of all objects of interest in a large image (object localisation) are potentially in conflict. We propose a Multi-Objective Genetic Programming (MOGP) approach to the task of providing a decision-maker with a diverse set of alternative object detection programs that balance between high detection rate and low false-alarm rate. Experiments on two datasets, simple shapes and photographs of coins, show that it is difficult for a Single-Objective GP (SOGP) system (which weights the multiple objectives a priori) to evolve effective object detectors, but that an MOGP system is able to evolve a range of effective object detectors more efficiently.