Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation 2010
DOI: 10.1145/1830483.1830728
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Performance evaluation of evolutionary algorithms for road detection

Abstract: In this paper we present the first comparative study of evolutionary classifiers for the problem of road detection. We use seven evolutionary algorithms (GAssist-ADI, XCS, UCS, cAnt, EvRBF,Fuzzy-AB and FuzzySLAVE ) for this purpose and to develop better understanding we also compare their performance with two well-known non-evolutionary classifiers (kNN, C4.5 ). Further we identify vision based features that enable a single classifier to learn to successfully classify a variety of regions in various roads as o… Show more

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“…Afridi et al [29] use seven evolutionary models for the problem of road detection and identify vision features that enable a single classifier to successfully classify regions of various roads as opposed to training a new classifier for each road type; Bousnguar et al [30] present a detection and recognition system, which first segments the image using a combination of RGB and HSV (hue saturation value) colors spaces and then searches for relevant shapes (circles, triangles, squares) using the Hough transform and corner detection with a support vector machine (SVM) while also Athrey et al [31] implement an algorithm for traffic sign detection based on thresholding, blob detection, and template matching.…”
Section: Related Workmentioning
confidence: 99%
“…Afridi et al [29] use seven evolutionary models for the problem of road detection and identify vision features that enable a single classifier to successfully classify regions of various roads as opposed to training a new classifier for each road type; Bousnguar et al [30] present a detection and recognition system, which first segments the image using a combination of RGB and HSV (hue saturation value) colors spaces and then searches for relevant shapes (circles, triangles, squares) using the Hough transform and corner detection with a support vector machine (SVM) while also Athrey et al [31] implement an algorithm for traffic sign detection based on thresholding, blob detection, and template matching.…”
Section: Related Workmentioning
confidence: 99%