7,049 research outputs found
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
Radio interferometric imaging of spatial structure that varies with time and frequency
The spatial-frequency coverage of a radio interferometer is increased by
combining samples acquired at different times and observing frequencies.
However, astrophysical sources often contain complicated spatial structure that
varies within the time-range of an observation, or the bandwidth of the
receiver being used, or both. Image reconstruction algorithms can been designed
to model time and frequency variability in addition to the average intensity
distribution, and provide an improvement over traditional methods that ignore
all variability. This paper describes an algorithm designed for such
structures, and evaluates it in the context of reconstructing three-dimensional
time-varying structures in the solar corona from radio interferometric
measurements between 5 GHz and 15 GHz using existing telescopes such as the
EVLA and at angular resolutions better than that allowed by traditional
multi-frequency analysis algorithms.Comment: 12 pages, 4 figures. SPIE Proceedings, Optical
Engineering+Applications; Image Reconstruction from Incomplete Dat
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