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EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION
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AI
DOI：10.22190/FUME190426038P.png)
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Abstract
En 中文
One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector.
Keywords:
Edge
Canny
Threshold
Optimal
Genetic Algorithm
Journal
F
IF:
11.8
Papers:
301
Citations:
1.6K