18 research outputs found
Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and Densenets can improve performance on Cifar-10+ consistently, while
having additional pleasant properties like steerability. By exploiting these
transformation properties explicitly, we arrive at dynamic steerable blocks.
They are an extension of residual blocks, that are able to seamlessly transform
filters under pre-defined transformations, conditioned on the input at training
and inference time. Dynamic steerable blocks learn the degree of invariance
from data and locally adapt filters, allowing them to apply a different
geometrical variant of the same filter to each location of the feature map.
When evaluated on the Berkeley Segmentation contour detection dataset, our
approach outperforms all competing approaches that do not utilize pre-training.
Our results highlight the benefits of image-based regularization to deep
networks
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lane detection methods rely on a combination of highly-specialized,
hand-crafted features and heuristics, usually followed by post-processing
techniques, that are computationally expensive and prone to scalability due to
road scene variations. More recent approaches leverage deep learning models,
trained for pixel-wise lane segmentation, even when no markings are present in
the image due to their big receptive field. Despite their advantages, these
methods are limited to detecting a pre-defined, fixed number of lanes, e.g.
ego-lanes, and can not cope with lane changes. In this paper, we go beyond the
aforementioned limitations and propose to cast the lane detection problem as an
instance segmentation problem - in which each lane forms its own instance -
that can be trained end-to-end. To parametrize the segmented lane instances
before fitting the lane, we further propose to apply a learned perspective
transformation, conditioned on the image, in contrast to a fixed "bird's-eye
view" transformation. By doing so, we ensure a lane fitting which is robust
against road plane changes, unlike existing approaches that rely on a fixed,
pre-defined transformation. In summary, we propose a fast lane detection
algorithm, running at 50 fps, which can handle a variable number of lanes and
cope with lane changes. We verify our method on the tuSimple dataset and
achieve competitive results
End-to-end Lane Detection through Differentiable Least-Squares Fitting
Lane detection is typically tackled with a two-step pipeline in which a
segmentation mask of the lane markings is predicted first, and a lane line
model (like a parabola or spline) is fitted to the post-processed mask next.
The problem with such a two-step approach is that the parameters of the network
are not optimized for the true task of interest (estimating the lane curvature
parameters) but for a proxy task (segmenting the lane markings), resulting in
sub-optimal performance. In this work, we propose a method to train a lane
detector in an end-to-end manner, directly regressing the lane parameters. The
architecture consists of two components: a deep network that predicts a
segmentation-like weight map for each lane line, and a differentiable
least-squares fitting module that returns for each map the parameters of the
best-fitting curve in the weighted least-squares sense. These parameters can
subsequently be supervised with a loss function of choice. Our method relies on
the observation that it is possible to backpropagate through a least-squares
fitting procedure. This leads to an end-to-end method where the features are
optimized for the true task of interest: the network implicitly learns to
generate features that prevent instabilities during the model fitting step, as
opposed to two-step pipelines that need to handle outliers with heuristics.
Additionally, the system is not just a black box but offers a degree of
interpretability because the intermediately generated segmentation-like weight
maps can be inspected and visualized. Code and a video is available at
github.com/wvangansbeke/LaneDetection_End2End.Comment: Accepted at ICCVW 2019 (CVRSUAD-Road Scene Understanding and
Autonomous Driving