报告详情
Automated Road Lane Detection Using Deep Learning
编号：190
访问权限：仅限参会人
更新：2025-12-23 13:40:08 浏览：24次
拓展类型2
报告开始：2025年12月30日 16:15（Asia/Amman）
报告时间：15min
所在会场：[S1] Track 1: Mobile computing, communications, 5G and beyond [S1-2] Track 1: Mobile computing, communications, 5G and beyond
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摘要
Automated Road Lane detection is an important component of modern driver support systems and autonomous vehicles. This paper presents an intensive learning-based approach to accurately detect and detect road lanes in real time. Methods of traditional lane detection methods rely on edge detection and Hough transform techniques, which are often sensitive to environmental conditions such as shadows, lighting and spread. To remove these boundaries, we appoint a firm nerve network (CNN) trained on a large dataset of road images. The proposed model effectively removes lane features and distinguishes them from other road signs and obstacles. We also use a post-processing technique to enhance detected lane structures and improve strength against challenging road conditions. Experimental results show that our intensive teaching approach is far ahead of traditional methods in terms of accuracy, adaptability and computational efficiency. This study highlights the ability to detect lanes for safe and more reliable autonomous driving systems.
关键词
Convolutional Neural Network, Automated Road Lane detection, Deep Learning
报告人
Surya Narayan Mishra
Professor Assistant Professor； India； School of Management； KIIT Deemed to be University稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
-
12月30日 2025
注册截止日期
-
12月31日 2025
初稿截稿日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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