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Universal Semantic Segmentation for Fisheye Urban Driving Images | | Yaozu Ye
; Kailun Yang
; Kaite Xiang
; Juan Wang
; Kaiwei Wang
; | | Date: |
31 Jan 2020 | | Abstract: | Semantic segmentation is a critical method in the field of autonomous
driving. When performing semantic image segmentation, a wider field of view
(FoV) helps to obtain more information about the surrounding environment,
making automatic driving safer and more reliable, which could be offered by
fisheye cameras. However, large public fisheye data sets are not available, and
the fisheye images captured by the fisheye camera with large FoV comes with
large distortion, so commonly-used semantic segmentation model cannot be
directly utilized. In this paper, a seven degrees of freedom (DoF) augmentation
method is proposed to transform rectilinear image to fisheye image in a more
comprehensive way. In the training process, rectilinear images are transformed
into fisheye images in seven DoF, which simulates the fisheye images taken by
cameras of different positions, orientations and focal lengths. The result
shows that training with the seven-DoF augmentation can evidently improve the
model’s accuracy and robustness against different distorted fisheye data. This
seven-DoF augmentation provides an universal semantic segmentation solution for
fisheye cameras in different autonomous driving applications. Also, we provide
specific parameter settings of the augmentation for autonomous driving. At
last, we tested our universal semantic segmentation model on real fisheye
images and obtained satisfactory results. The code and configurations are
released at url{this https URL}. | | Source: | arXiv, 2002.3736 | | Services: | Forum | Review | PDF | Favorites | |
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