scholarly journals Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 107958-107968
Author(s):  
Xiaoliang Wang ◽  
Yeqiang Qian ◽  
Chunxiang Wang ◽  
Ming Yang
Keyword(s):  
Lane Detection ◽  
Missing Feature

Lane detection algorithm based on morphological structure-elements model

2009 ◽  
Vol 29 (2) ◽  
pp. 440-443 ◽  
Author(s):  
Tao LEI ◽  
Yang-yu FAN ◽  
Xiao-peng WANG ◽  
Lü-cheng WANG
Keyword(s):  
Morphological Structure ◽  
Detection Algorithm ◽  
Lane Detection

Visibility estimation on road based on lane detection and image inflection

2013 ◽  
Vol 32 (12) ◽  
pp. 3397-3403
Author(s):  
Hong-jun SONG ◽  
Yang-zhou CHEN ◽  
Yuan-yuan GAO
Keyword(s):  
Lane Detection ◽  
Visibility Estimation

scholarly journals Are you really using Yarning research? Mapping Social and Family Yarning to strengthen Yarning research quality

2021 ◽  
pp. 117718012110154
Author(s):  
Petah Atkinson ◽  
Marilyn Baird ◽  
Karen Adams
Keyword(s):  
Research Method ◽  
Research Question ◽  
Research Quality ◽  
Aboriginal Community ◽  
Participant Recruitment ◽  
The Social ◽  
Research Findings ◽  
Convenience Sampling ◽  
Missing Feature ◽  
Published Research

Yarning as a research method has its grounding as an Aboriginal culturally specified process. Significant to the Research Yarn is relationality, however; this is a missing feature of published research findings. This article aims to address this. The research question was, what can an analysis of Social and Family Yarning tell us about relationality that underpins a Research Yarn. Participant recruitment occurred using convenience sampling, and data collection involved Yarning method. Five steps of data analysis occurred featuring Collaborative Yarning and Mapping. Commonality existed between researcher and participants through predominantly experiences of being a part of Aboriginal community, via Aboriginal organisations and Country. This suggests shared explicit and tacit knowledge and generation of thick data. Researchers should report on their experience with Yarning, the types of Yarning they are using, and the relationality generated from the Social, Family and Research Yarn.

scholarly journals A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 708
Author(s):  
Wenbo Liu ◽  
Fei Yan ◽  
Jiyong Zhang ◽  
Tao Deng
Keyword(s):  
Information Exchange ◽  
Information Transfer ◽  
Spatial Information ◽  
Contextual Information ◽  
Great Influence ◽  
Light Condition ◽  
Lane Detection ◽  
Detection Model ◽  
Spatial Features ◽  
Proposed Model

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

Deep reinforcement learning based lane detection and localization

2020 ◽  
Vol 413 ◽  
pp. 328-338 ◽  
Author(s):  
Zhiyuan Zhao ◽  
Qi Wang ◽  
Xuelong Li
Keyword(s):  
Reinforcement Learning ◽  
Lane Detection ◽  
Detection And Localization

scholarly journals On-CMOS Image Sensor Processing for Lane Detection

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3713
Author(s):  
Soyeon Lee ◽  
Bohyeok Jeong ◽  
Keunyeol Park ◽  
Minkyu Song ◽  
Soo Youn Kim
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Image Resolution ◽  
Lane Detection ◽  
Frame Rate ◽  
Total Power ◽  
Detection Accuracy ◽  
Total Power Consumption

This paper presents a CMOS image sensor (CIS) with built-in lane detection computing circuits for automotive applications. We propose on-CIS processing with an edge detection mask used in the readout circuit of the conventional CIS structure for high-speed lane detection. Furthermore, the edge detection mask can detect the edges of slanting lanes to improve accuracy. A prototype of the proposed CIS was fabricated using a 110 nm CIS process. It has an image resolution of 160 (H) × 120 (V) and a frame rate of 113, and it occupies an area of 5900 μm × 5240 μm. A comparison of its lane detection accuracy with that of existing edge detection algorithms shows that it achieves an acceptable accuracy. Moreover, the total power consumption of the proposed CIS is 9.7 mW at pixel, analog, and digital supply voltages of 3.3, 3.3, and 1.5 V, respectively.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

Sign in / Sign up

Export Citation Format

Share Document