Efficient fine-grained road segmentation using superpixel-based CNN and CRF models. (arXiv:2207.02844v1 [cs.CV])

Towards a safe and comfortable driving, road scene segmentation is a
rudimentary problem in camera-based advance driver assistance systems (ADAS).
Despite of the great achievement of Convolutional Neural Networks (CNN) for
semantic segmentation task, the high computational efforts of CNN based methods
is still a challenging area. In recent work, we proposed a novel approach to
utilise the advantages of CNNs for the task of road segmentation at reasonable
computational effort. The runtime benefits from using irregular super pixels as
basis for the input for the CNN rather than the image grid, which tremendously
reduces the input size. Although, this method achieved remarkable low
computational time in both training and testing phases, the lower resolution of
the super pixel domain yields naturally lower accuracy compared to high cost
state of the art methods. In this work, we focus on a refinement of the road
segmentation utilising a Conditional Random Field (CRF).The refinement
procedure is limited to the super pixels touching the predicted road boundary
to keep the additional computational effort low. Reducing the input to the
super pixel domain allows the CNNs structure to stay small and efficient to
compute while keeping the advantage of convolutional layers and makes them
eligible for ADAS. Applying CRF compensate the trade off between accuracy and
computational efficiency. The proposed system obtained comparable performance
among the top performing algorithms on the KITTI road benchmark and its fast
inference makes it particularly suitable for realtime applications.

Source: https://arxiv.org/abs/2207.02844


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