Abstract:
Intelligent driving systems must detect lane markings. It helps with vehicle positioning, forehead car detection, and road condition information to prevent lane departure. Extreme lighting, missing lane markings, and obstacles make lane marking detection difficult. Due to their performance, deep learning-based algorithms have garnered attention in intelligent driving society. We review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, which determine their success. We also review lane-related datasets, evaluation criteria, and data processing methods. We also compare the detection performance and running time of various methods and discuss deep learning-based lane marking detection algorithm challenges and future trends.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
