Lane and Traffic Sign Detection in Self-Driving Cars using Deep Learning
Main Article Content
Abstract
With artificial intelligence technology progressing at a tremendous speed, intelligent driving has got a lot of recognition in recent years. Lane detection is one of the primary functions in self-driving cars. Traditionally, lane detection was done using image processing algorithms and computer vision techniques, which included extraction of areas which are possible lane areas, edge enhancement etc. Deep learning models with new improvements are being introduced till date. Additionally, a self-driving vehicle must be able to recognise traffic signs. In the proposed work a VGG-16 convolutional neural network is used for road segmentation. The model is trained on the KITTI road/lane detection evaluation 2013 dataset. The model performed well with an accuracy of 98.58%. For traffic sign detection, the German traffic sign recognition benchmark dataset is used. A convolutional neural network is used with ADAM optimizer, which gives an accuracy of 95%.
Article Details
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms: a. Authors retain copyright and grant the journal right of first publication, with the work two years after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).