In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 10 ( 2021-10-19), p. e0252755-
Abstract:
The security of car driving is of interest due to the growing number of motor vehicles and frequent occurrence of road traffic accidents, and the combination of advanced driving assistance system (ADAS) and vehicle-road cooperation can prevent more than 90% of traffic accidents. Lane detection, as a vital part of ADAS, has poor real-time performance and accuracy in multiple scenarios, such as road damage, light changes, and traffic jams. Moreover, the sparse pixels of lane lines on the road pose a tremendous challenge to the task of lane line detection. In this study, we propose a model that fuses non bottleneck skip residual connections and an improved attention pyramid (IAP) to effectively obtain contextual information about real-time scenes and improve the robustness and real-time performance of current lane detection models. The proposed model modifies the efficient residual factorized pyramid scene parsing network (ERF-PSPNet) and utilizes skip residual connections in non bottleneck-1D modules. A decoder with an IAP provides high-level feature maps with pixel-level attention. We add an auxiliary segmenter and a lane predictor side-by-side after the encoder, the former for lane prediction and the latter to assist with semantic segmentation for classification purposes, as well as to solve the gradient disappearance problem. On the CULane dataset, the F1 metric reaches 92.20% in the normal scenario, and the F1 metric of the model is higher than the F1 metrics of other existing models, such as ERFNet-HESA, ENet_LGAD, and DSB+LDCDI, in normal, crowded, night, dazzling light and no line scenarios; in addition, the mean F1 of the nine scenarios reached 74.10%, the runtime (time taken to test 100 images) of the model was 5.88 ms, and the number of parameters was 2.31M, which means that the model achieves a good trade-off between real-time performance and accuracy compared to the current best results (i.e., a running time of 13.4 ms and 0.98M parameters).
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0252755
DOI:
10.1371/journal.pone.0252755.g001
DOI:
10.1371/journal.pone.0252755.g002
DOI:
10.1371/journal.pone.0252755.g003
DOI:
10.1371/journal.pone.0252755.g004
DOI:
10.1371/journal.pone.0252755.g005
DOI:
10.1371/journal.pone.0252755.g006
DOI:
10.1371/journal.pone.0252755.g007
DOI:
10.1371/journal.pone.0252755.g008
DOI:
10.1371/journal.pone.0252755.g009
DOI:
10.1371/journal.pone.0252755.g010
DOI:
10.1371/journal.pone.0252755.g011
DOI:
10.1371/journal.pone.0252755.g012
DOI:
10.1371/journal.pone.0252755.g013
DOI:
10.1371/journal.pone.0252755.t001
DOI:
10.1371/journal.pone.0252755.t002
DOI:
10.1371/journal.pone.0252755.t003
DOI:
10.1371/journal.pone.0252755.t004
DOI:
10.1371/journal.pone.0252755.t005
DOI:
10.1371/journal.pone.0252755.s001
DOI:
10.1371/journal.pone.0252755.s002
DOI:
10.1371/journal.pone.0252755.s003
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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