A lane detection approach based on intelligent vision

2015 ◽  
Vol 42 ◽  
pp. 23-29 ◽  
Author(s):  
Shu-Chung Yi ◽  
Yeong-Chin Chen ◽  
Ching-Haur Chang
Keyword(s):  
Lane Detection ◽  
Detection Approach

scholarly journals Vision-Based Lane Departure Detection Using a Stacked Sparse Autoencoder

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zengcai Wang ◽  
Xiaojin Wang ◽  
Lei Zhao ◽  
Guoxin Zhang
Keyword(s):  
Polar Angle ◽  
Region Of Interest ◽  
Lane Detection ◽  
Fine Tuning ◽  
High Detection Rate ◽  
Softmax Classifier ◽  
Lane Departure ◽  
Detection Approach ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

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.

scholarly journals Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

2020 ◽  
Vol 10 (7) ◽  
pp. 2543 ◽  
Author(s):  
Jianjun Hu ◽  
Songsong Xiong ◽  
Yuqi Sun ◽  
Junlin Zha ◽  
Chunyun Fu
Keyword(s):  
Region Of Interest ◽  
Global Search ◽  
Lane Detection ◽  
Vehicle Speed ◽  
Feature Points ◽  
The Road ◽  
Detection Approach ◽  
Polar Coordinate ◽  
Previous Frame ◽  
Lane Line

A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.

scholarly journals A Computer Vision based Lane Detection Approach

2019 ◽  
Vol 11 (3) ◽  
pp. 27-34 ◽  
Author(s):  
Md. Rezwanul Haque ◽  
◽  
Md. Milon Islam ◽  
Kazi Saeed Alam ◽  
Hasib Iqbal
Keyword(s):  
Computer Vision ◽  
Lane Detection ◽  
Detection Approach

A deep learning based fast lane detection approach

2022 ◽  
Vol 155 ◽  
pp. 111722
Author(s):  
Erkan Oğuz ◽  
Ayhan Küçükmanisa ◽  
Ramazan Duvar ◽  
Oğuzhan Urhan
Keyword(s):  
Deep Learning ◽  
Lane Detection ◽  
Detection Approach ◽  
Fast Lane

scholarly journals Robust and real-time multi-lane and single lane detection in Indian highway scenarios

2021 ◽  
Vol 309 ◽  
pp. 01016
Author(s):  
A. Sai Hanuman ◽  
G. Prasanna Kumar
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Region Of Interest ◽  
Weather Conditions ◽  
Vital Role ◽  
Lane Detection ◽  
Fundamental Aspect ◽  
Detection Approach ◽  
Lane Line ◽  
Canny Edge

In the Advanced Driver Assistance System (ADAS), lane detection plays a vital role to avoid road accidents of an Autonomous vehicle. Also, autonomous vehicles should be able to navigate by themselves, in-order to do, it needs to understand its surrounding conditions like a human. So that vehicle can determine its path in streets and highways it can maintain lane manoeuvre. Also, It has become the most fundamental aspect to consider in current ADAS research. One of the major hurdles in self-driving vehicle research is identifying the curved lanes, multiple lanes with challenging light, and weather conditions, especially in Indian highway scenarios. As it is a vision-based lane detection approach we are using OpenCV library which consists of multiple algorithms like the optimization of canny edge detection to find out the edges, features of the lane and Hough Transform for lane line generation and apply on the particular region of interest.

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