Abstract
The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.
Highlights
The task of detecting lines and lanes was mainly developed in the field of ADAS
ADAS were passive, but with new tools, these systems allow for more advanced tasks, i.e., active assistance
The analysis of the proposed shows the limitation of the use of individual algorithms is the possibility of their use in embedded systems
Summary
Low. The task of detecting lines and lanes was mainly developed in the field of ADAS (advanced driver assistance systems). ADAS were passive, but with new tools, these systems allow for more advanced tasks, i.e., active assistance. The subject of line detection is still valid in scientific papers [1,2]. They may not be effective under specific conditions; for example, in the detection of airport runway lines that require precise driving of measurement equipment, such as mobile measurement trailers for checking the correctness of light intensity of airport lamps [3,4]
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