2,351 research outputs found
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Deep learning techniques have enabled the emergence of state-of-the-art
models to address object detection tasks. However, these techniques are
data-driven, delegating the accuracy to the training dataset which must
resemble the images in the target task. The acquisition of a dataset involves
annotating images, an arduous and expensive process, generally requiring time
and manual effort. Thus, a challenging scenario arises when the target domain
of application has no annotated dataset available, making tasks in such
situation to lean on a training dataset of a different domain. Sharing this
issue, object detection is a vital task for autonomous vehicles where the large
amount of driving scenarios yields several domains of application requiring
annotated data for the training process. In this work, a method for training a
car detection system with annotated data from a source domain (day images)
without requiring the image annotations of the target domain (night images) is
presented. For that, a model based on Generative Adversarial Networks (GANs) is
explored to enable the generation of an artificial dataset with its respective
annotations. The artificial dataset (fake dataset) is created translating
images from day-time domain to night-time domain. The fake dataset, which
comprises annotated images of only the target domain (night images), is then
used to train the car detector model. Experimental results showed that the
proposed method achieved significant and consistent improvements, including the
increasing by more than 10% of the detection performance when compared to the
training with only the available annotated data (i.e., day images).Comment: 8 pages, 8 figures,
https://github.com/viniciusarruda/cross-domain-car-detection and accepted at
IJCNN 201
A Model-Predictive Motion Planner for the IARA Autonomous Car
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent
Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses
a path planner to compute a path from its current position to the desired
destination. Using this path, the current position, a goal in the path and a
map, IARA's MPMP is able to compute smooth trajectories from its current
position to the goal in less than 50 ms. MPMP computes the poses of these
trajectories so that they follow the path closely and, at the same time, are at
a safe distance of eventual obstacles. Our experiments have shown that MPMP is
able to compute trajectories that precisely follow a path produced by a Human
driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of
up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on
Robotics and Automation (ICRA
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