Lane recognition in unstructured environments is the basis for vision-based navigation of mobile robots.
非结构化环境的道路分割是移动机器人视觉导航的一个重要研究内容。
This paper presents a lane recognition method which can satisfy the high-speed requirement of outdoor mobile robots.
提出了一种可满足室外移动机器人高速行驶要求的车道线检测识别方法。
Thee impact sent the sedan flying into the guardrail before it came to rest in the right lane of the road and burst into flames, burning its occupants beyond recognition.
碰撞造成轿车冲出护栏，然后停在右车道燃烧起来，车内人员被烧焦并难以辨认。
In this paper, a multi layer template correlation neural network (MTCNN) based on the pipelined image processing structure is proposed for the recognition of lane mark.
本文提出了一种便于流水线图像处理结构实现的多层模板相关神经元网络(MTCNN)。
This paper presents an algorithm combining scan line and region-growing method based on video images analysis, in which lane-marking recognition and driving deviation detection are realized.
为了实时检测到行车偏移信息，提出了一种基于扫描线与区域生长相结合的视频图像分析算法，从而实现了多种道路车道标线和行车偏移的自动检测。
This paper presents an algorithm combining scan line and region-growing method based on video images analysis, in which lane-marking recognition and driving deviation detection are realized.
为了实时检测到行车偏移信息，提出了一种基于扫描线与区域生长相结合的视频图像分析算法，从而实现了多种道路车道标线和行车偏移的自动检测。
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