74,267 research outputs found
LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles
A key capability for autonomous underground mining vehicles is real-time
accurate localisation. While significant progress has been made, currently
deployed systems have several limitations ranging from dependence on costly
additional infrastructure to failure of both visual and range sensor-based
techniques in highly aliased or visually challenging environments. In our
previous work, we presented a lightweight coarse vision-based localisation
system that could map and then localise to within a few metres in an
underground mining environment. However, this level of precision is
insufficient for providing a cheaper, more reliable vision-based automation
alternative to current range sensor-based systems. Here we present a new
precision localisation system dubbed "LookUP", which learns a
neural-network-based pixel sampling strategy for estimating homographies based
on ceiling-facing cameras without requiring any manual labelling. This new
system runs in real time on limited computation resource and is demonstrated on
two different underground mine sites, achieving real time performance at ~5
frames per second and a much improved average localisation error of ~1.2 metre.Comment: 7 pages, 7 figures, accepted for IEEE ICRA 201
A deep learning framework for quality assessment and restoration in video endoscopy
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.Comment: 14 page
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