Object Detection on Spatially Inhomogeneous Backgrounds Using Neural Networks


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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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