Object Detection on Spatially Inhomogeneous Backgrounds Using Neural Networks

Resumo

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.