计算机科学
钥匙(锁)
分割
聚类分析
人工智能
点(几何)
直线(几何图形)
编码(集合论)
数据挖掘
机器学习
集合(抽象数据类型)
数学
计算机安全
几何学
程序设计语言
作者
Yeongmin Ko,Younkwan Lee,Shoaib Azam,Farzeen Munir,Moongu Jeon,Witold Pedrycz
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-07-01
卷期号:23 (7): 8949-8958
被引量:87
标识
DOI:10.1109/tits.2021.3088488
摘要
Perception techniques for autonomous driving should be adaptive to various environments. In essential perception modules for traffic line detection, many conditions should be considered, such as a number of traffic lines and computing power of the target system. To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach. The PINet includes several hourglass models that are trained simultaneously with the same loss function. Therefore, the size of the trained models can be chosen according to the target environment’s computing power. We cast a clustering problem of the predicted key points as an instance segmentation problem; the PINet can be trained regardless of the number of the traffic lines. The PINet achieves competitive accuracy and false positive on CULane and TuSimple datasets, popular public datasets for lane detection. Our code is available at https://github.com/koyeongmin/PINet_new
科研通智能强力驱动
Strongly Powered by AbleSci AI