Improved detection of small objects in road network sequences
- URL: http://arxiv.org/abs/2105.08416v1
- Date: Tue, 18 May 2021 10:13:23 GMT
- Title: Improved detection of small objects in road network sequences
- Authors: Iv\'an Garc\'ia, Rafael Marcos Luque and Ezequiel L\'opez
- Abstract summary: We propose a new procedure for detecting small-scale objects by applying super-resolution processes based on detections performed by convolutional neural networks.
This work has been tested for a set of traffic images containing elements of different scales to test the efficiency according to the detections obtained by the model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The vast number of existing IP cameras in current road networks is an
opportunity to take advantage of the captured data and analyze the video and
detect any significant events. For this purpose, it is necessary to detect
moving vehicles, a task that was carried out using classical artificial vision
techniques until a few years ago. Nowadays, significant improvements have been
obtained by deep learning networks. Still, object detection is considered one
of the leading open issues within computer vision.
The current scenario is constantly evolving, and new models and techniques
are appearing trying to improve this field. In particular, new problems and
drawbacks appear regarding detecting small objects, which correspond mainly to
the vehicles that appear in the road scenes. All this means that new solutions
that try to improve the low detection rate of small elements are essential.
Among the different emerging research lines, this work focuses on the detection
of small objects. In particular, our proposal aims to vehicle detection from
images captured by video surveillance cameras.
In this work, we propose a new procedure for detecting small-scale objects by
applying super-resolution processes based on detections performed by
convolutional neural networks \emph{(CNN)}. The neural network is integrated
with processes that are in charge of increasing the resolution of the images to
improve the object detection performance. This solution has been tested for a
set of traffic images containing elements of different scales to test the
efficiency according to the detections obtained by the model, thus
demonstrating that our proposal achieves good results in a wide range of
situations.
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