Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational
intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is
finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest
in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop
signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the
GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of
this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for
the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential
velocity decay function to the PSO, the addition of an ”execution best” omnipresent particle to the PSO, and the addition
of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using
various combinations of selection, crossover, and mutation operators and experimentation was also performed with the
PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence
scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and
drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful
problem specific parameter sets.
Jeremy E. Davis, Amy E. Bednar, Christopher T. Goodin, Phillip J. Durst, Derek T. Anderson, and Cindy L. Bethel, "Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection," Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000V (Presented at SPIE Defense + Security: April 12, 2017; Published: 2 May 2017); https://doi.org/10.1117/12.2262259.
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