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Heading Homing Simulation System Based on Image Intelligent Recognition

Journal from gdlhub / 2016-11-09 06:11:20
Oleh : Zhao Ke, Ziba Eslami, Telkomnika
Dibuat : 2016-06-01, dengan 1 file

Keyword : Machine vision; Channel line recognition; B-Spline; Curve fitting; Random sampling consistency; Embedded system
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4346

Channel line recognition is one of extremely important technologies in intelligent driving field. In recent years, machine vision has been the mainstream method to solve channel line recognition problems. To overcome such deficiencies of existing channel line recognition algorithms as being complicated, slow and short of robustness, the Thesis provides a new and rapid channel line recognition algorithm which firstly obtains outline pixel of channel line through analysis on the images’ grayscale differences and then applies B-Spline curve to fit the channel line profile, thus getting the final recognition effect picture. The experiments show that excellent performances in both speed and recognition rate can result from the algorithm. Besides, in embedded platform, the speed of the algorithm in the Thesis results in 12 frames per second, which conforms to the real demands of intelligent driving.

Deskripsi Alternatif :

Channel line recognition is one of extremely important technologies in intelligent driving field. In recent years, machine vision has been the mainstream method to solve channel line recognition problems. To overcome such deficiencies of existing channel line recognition algorithms as being complicated, slow and short of robustness, the Thesis provides a new and rapid channel line recognition algorithm which firstly obtains outline pixel of channel line through analysis on the images’ grayscale differences and then applies B-Spline curve to fit the channel line profile, thus getting the final recognition effect picture. The experiments show that excellent performances in both speed and recognition rate can result from the algorithm. Besides, in embedded platform, the speed of the algorithm in the Thesis results in 12 frames per second, which conforms to the real demands of intelligent driving.

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