Robust Spatiotemporal Lane Detection Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50021769" target="_blank" >RIV/62690094:18450/25:50021769 - isvavai.cz</a>

Výsledek na webu

<a href="https://journals.sagepub.com/doi/10.1177/03611981241260696" target="_blank" >https://journals.sagepub.com/doi/10.1177/03611981241260696</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/03611981241260696" target="_blank" >10.1177/03611981241260696</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Spatiotemporal Lane Detection Model

Popis výsledku v původním jazyce

Lane lines are frequently interrupted in autonomous driving environments because of some objective conditions, such as occlusion or congestion, which often lead to the decreased detection performance of a model. Current detection methods relying on spatial information struggle to detect complete lane lines in such conditions. In this paper, we build a robust lane detection model by fusing spatiotemporal information and dilated convolution. The proposed model is aided by the dilated convolution, which expands the scope of convolutional processes to extract more lane feature information from various perception environments. Convolutional gate recurrent units (ConvGRUs) are employed at the high-level semantic phase to aid the proposed model to get more effective lane feature information by dealing with the spatiotemporal information of consecutive frames. Compared with models FCN, DeepLabv3, RefineNet, SCNN, Cheng-DET, LDNet, SegNet, SegNet-Ego-Lane, Res18, Res34, ResNet-18-SAD, ResNet-34-SAD, ENet-SAD, ReNet-101, R-18-E2E, R-34-E2E, R-101-SAD, R-101-E2E, ResNet34-Qin, LaneNet, PINET(64x32), UNet_ConvLSTMSegNet_ConvLSTM, LDSTNet, extensive experiments on three well-known lane detection benchmarks prove the usefulness of the proposed model, achieving robust results and competitive performance.

Název v anglickém jazyce

Robust Spatiotemporal Lane Detection Model

Popis výsledku anglicky

Lane lines are frequently interrupted in autonomous driving environments because of some objective conditions, such as occlusion or congestion, which often lead to the decreased detection performance of a model. Current detection methods relying on spatial information struggle to detect complete lane lines in such conditions. In this paper, we build a robust lane detection model by fusing spatiotemporal information and dilated convolution. The proposed model is aided by the dilated convolution, which expands the scope of convolutional processes to extract more lane feature information from various perception environments. Convolutional gate recurrent units (ConvGRUs) are employed at the high-level semantic phase to aid the proposed model to get more effective lane feature information by dealing with the spatiotemporal information of consecutive frames. Compared with models FCN, DeepLabv3, RefineNet, SCNN, Cheng-DET, LDNet, SegNet, SegNet-Ego-Lane, Res18, Res34, ResNet-18-SAD, ResNet-34-SAD, ENet-SAD, ReNet-101, R-18-E2E, R-34-E2E, R-101-SAD, R-101-E2E, ResNet34-Qin, LaneNet, PINET(64x32), UNet_ConvLSTMSegNet_ConvLSTM, LDSTNet, extensive experiments on three well-known lane detection benchmarks prove the usefulness of the proposed model, achieving robust results and competitive performance.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

TRANSPORTATION RESEARCH RECORD

ISSN

0361-1981

e-ISSN

2169-4052

Svazek periodika

2679

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

2213-2227

Kód UT WoS článku

001288888500001

EID výsledku v databázi Scopus

2-s2.0-85201019299