Date of Award:
5-2017
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Vladimir Kulyukin
Committee
Vladimir Kulyukin
Committee
Dan Watson
Committee
Nicholas Flann
Abstract
Rapid progress is being made towards the realization of autonomous cars. Since the technology is in its early stages, human intervention is still necessary in order to ensure hazard-free operation of autonomous driving systems. Substantial research efforts are underway to enhance driver and passenger safety in autonomous cars. Toward that end GreedyHaarSpiker, a real-time vision-based lane detection algorithm is proposed for road lane detection in different weather conditions. The algorithm has been implemented in Python 2.7 with OpenCV 3.0 and tested on a Raspberry Pi 3 Model B ARMv8 1GB RAM coupled to a Raspberry Pi camera board v2. To test the algorithm’s performance, the Raspberry Pi and the camera board were mounted inside a Jeep Wrangler. The algorithm performed better in sunny weather with no snow on the road. The algorithm’s performance deteriorated at night time or when the road surface was covered with snow.
Checksum
41a5d6d9fb11301c6906f90a7020716c
Recommended Citation
Sudini, Vikas Reddy, "Real-time Vision-Based Lane Detection with 1D Haar Wavelet Transform on Raspberry Pi" (2017). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 5630.
https://digitalcommons.usu.edu/etd/5630
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