Object Detection on Spatially Inhomogeneous Backgrounds Using Neural Networks
- Authors: Shakenov A.K.1
-
Affiliations:
- Institute of Automation and Electrometry, Siberian Branch
- Issue: Vol 55, No 6 (2019)
- Pages: 587-591
- Section: Analysis and Synthesis of Signals and Images
- URL: https://journals.rcsi.science/8756-6990/article/view/212919
- DOI: https://doi.org/10.3103/S8756699019060086
- ID: 212919
Cite item
Abstract
Several approaches to the use of neural networks for object detection on spatially inhomogeneous backgrounds are considered. A method for constructing a classifier for object detection directly from observed fragments has been developed. An approach consisting of a combination of matched linear filtering and convolutional neural networks is proposed. It is shown that this approach reduces the false alarm probability while maintaining the object detection probability.
About the authors
A. K. Shakenov
Institute of Automation and Electrometry, Siberian Branch
Author for correspondence.
Email: adil.shakenov@ngs.ru
Russian Federation, pr. Akademika Koptyuga 1, Novosibirsk, 630090
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