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공지사항
논문명 |
Perspective Transformer and MobileNets-Based 3D Lane Detection from Single 2D Image |
논문종류 |
SCI |
저자 |
Mengyu Li , Phuong Minh Chu and Kyungeun Cho |
Impact Factor |
2.592 |
게재학술지명 |
Mathematics |
게재일 |
2022.10 |
Three-dimensional (3D) lane detection is widely used in image understanding, image
analysis, 3D scene reconstruction, and autonomous driving. Recently, various methods for 3D lane
detection from single two-dimensional (2D) images have been proposed to address inaccurate lane
layouts in scenarios (e.g., uphill, downhill, and bumps). Many previous studies struggled in solving
complex cases involving realistic datasets. In addition, these methods have low accuracy and high
computational resource requirements. To solve these problems, we put forward a high-quality
method to predict 3D lanes from a single 2D image captured by conventional cameras, which is also
cost effective. The proposed method comprises the following three stages. First, a MobileNet model
that requires low computational resources was employed to generate multiscale front-view features
from a single RGB image. Then, a perspective transformer calculated bird’s eye view (BEV) features
from the front-view features. Finally, two convolutional neural networks were used for predicting
the 2D and 3D coordinates and respective lane types. The results of the high-reliability experiments
verified that our method achieves fast convergence and provides high-quality 3D lanes from single
2D images. Moreover, the proposed method requires no exceptional computational resources, thereby
reducing its implementation costs. |
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