REAL TIME LANE DETECTION USING ROSS KIPPENBROCK METHOD AND OBJECT DETECTION USING YOLO

Clicks: 176
ID: 109692
2020
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Abstract
The aim is to detect lane and object with higher accuracy rate, even in different climatic conditions. Using Ross kippenbrock principle for finding lane (Advance lane detection) and YOLO for object detection which has a higher speed and accuracy rate. Self-driving cars might not be in our everyday lives yet, but they are coming. Analyzing images and figuring out where the lane lines are on a given roadway is one of the core competencies of any respectable self-driving car. After getting the output for the lane detection, these frames are sent to the CNN model using YOLO (you only look once) which will detect the object which are present in the frame. These two methods gives a higher accurate and spped result, which is must for self-driving car.
Reference Key
Aditham2020internationalREAL Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Harshitha Aditham;Rajeshwari Nadar;
Journal international engineering journal for research & development
Year 2020
DOI
10.17605/OSF.IO/RFM45
URL
http://iejrd.com/index.php/%20/article/view/684
Keywords
self driving car advance lane detection yolo

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