Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation

Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Guansheng Xing, Ziming Zhu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lane and road marker segmentation mask cropping optical flow estimation semantic video segmentation temporal consistency Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/80c5503b8e5542fcbbb02eaee20d2f47
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:80c5503b8e5542fcbbb02eaee20d2f47
record_format	dspace
spelling	oai:doaj.org-article:80c5503b8e5542fcbbb02eaee20d2f472021-11-11T19:09:09ZLane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation10.3390/s212171561424-8220https://doaj.org/article/80c5503b8e5542fcbbb02eaee20d2f472021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7156https://doaj.org/toc/1424-8220Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.Guansheng XingZiming ZhuMDPI AGarticlelane and road marker segmentationmask croppingoptical flow estimationsemantic video segmentationtemporal consistencyChemical technologyTP1-1185ENSensors, Vol 21, Iss 7156, p 7156 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lane and road marker segmentation mask cropping optical flow estimation semantic video segmentation temporal consistency Chemical technology TP1-1185
spellingShingle	lane and road marker segmentation mask cropping optical flow estimation semantic video segmentation temporal consistency Chemical technology TP1-1185 Guansheng Xing Ziming Zhu Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation
description	Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.
format	article
author	Guansheng Xing Ziming Zhu
author_facet	Guansheng Xing Ziming Zhu
author_sort	Guansheng Xing
title	Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation
title_short	Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation
title_full	Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation
title_fullStr	Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation
title_full_unstemmed	Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation
title_sort	lane and road marker semantic video segmentation using mask cropping and optical flow estimation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/80c5503b8e5542fcbbb02eaee20d2f47
work_keys_str_mv	AT guanshengxing laneandroadmarkersemanticvideosegmentationusingmaskcroppingandopticalflowestimation AT zimingzhu laneandroadmarkersemanticvideosegmentationusingmaskcroppingandopticalflowestimation
_version_	1718431594569531392

Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation

Ejemplares similares