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Interactive Attention Learning on Detection of Lane and Lane Marking on the Road by Monocular Camera Image

Tian, Wei; Yu, Xianwang; Hu, Haohao 1
1 Institut für Mess- und Regelungstechnik (MRT), Karlsruher Institut für Technologie (KIT)

Abstract:

Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks. In this paper, we establish a novel multi-task encoder–decoder framework for the simultaneous detection of lanes and lane markings. This approach deploys a dual-branch architecture to extract image information from different scales. By revealing the spatial constraints between lanes and lane markings, we propose an interactive attention learning for their feature information, which involves a Deformable Feature Fusion module for feature encoding, a Cross-Context module as information decoder, a Cross-IoU loss and a Focal-style loss weighting for robust training. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000161399
Veröffentlicht am 15.08.2023
Originalveröffentlichung
DOI: 10.3390/s23146545
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik (MRT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000161399
Erschienen in Sensors
Verlag MDPI
Band 23
Heft 14
Seiten Art.-Nr.: 6545
Vorab online veröffentlicht am 20.07.2023
Schlagwörter interactive attention learning, lane segmentation, lane marking detection
Nachgewiesen in Web of Science
Dimensions
Scopus
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