Abstract
We describe an approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large pictures must be found. The evolved programs use a feature set computed from a square input field large enough to contain each of objects of interest and are applied, in moving window fashion, over the large pictures in order to locate the objects of interest. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty with four different classes of interest. On pictures of easy and medium difficulty all objects are detected with no false alarms. On difficult pictures there are still significant numbers of errors, however the results are considerably better than those of a neural network based program for the same problems.
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Zhang, M., Ciesielski, V. (1999). Genetic Programming for Multiple Class Object Detection. In: Foo, N. (eds) Advanced Topics in Artificial Intelligence. AI 1999. Lecture Notes in Computer Science(), vol 1747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46695-9_16
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DOI: https://doi.org/10.1007/3-540-46695-9_16
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