Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder
Joint Authors
Wang, Zengcai
Zhang, Guoxin
Zhao, Lei
Wang, Xiaojin
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-16
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
This paper presents a lane departure detection approach that utilizes a stacked sparse autoencoder (SSAE) for vehicles driving on motorways or similar roads.
Image preprocessing techniques are successfully executed in the initialization procedure to obtain robust region-of-interest extraction parts.
Lane detection operations based on Hough transform with a polar angle constraint and a matching algorithm are then implemented for two-lane boundary extraction.
The slopes and intercepts of lines are obtained by converting the two lanes from polar to Cartesian space.
Lateral offsets are also computed as an important step of feature extraction in the image pixel coordinate without any intrinsic or extrinsic camera parameter.
Subsequently, a softmax classifier is designed with the proposed SSAE.
The slopes and intercepts of lines and lateral offsets are the feature inputs.
A greedy, layer-wise method is employed based on the inputs to pretrain the weights of the entire deep network.
Fine-tuning is conducted to determine the global optimal parameters by simultaneously altering all layer parameters.
The outputs are three detection labels.
Experimental results indicate that the proposed approach can detect lane departure robustly with a high detection rate.
The efficiency of the proposed method is demonstrated on several real images.
American Psychological Association (APA)
Wang, Zengcai& Wang, Xiaojin& Zhao, Lei& Zhang, Guoxin. 2018. Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1209847
Modern Language Association (MLA)
Wang, Zengcai…[et al.]. Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder. Mathematical Problems in Engineering No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1209847
American Medical Association (AMA)
Wang, Zengcai& Wang, Xiaojin& Zhao, Lei& Zhang, Guoxin. Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1209847
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209847