Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

Amir Soleimani; Nasser M. Nasrabadi; Elias Griffith; Jason Ralph; Simon Maskell

Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

Computer Vision and Pattern Recognition 2018-08-28 v1

Authors: Amir Soleimani , Nasser M. Nasrabadi , Elias Griffith , Jason Ralph , Simon Maskell

Abstract

This paper investigates the problem of aerial vehicle recognition using a text-guided deep convolutional neural network classifier. The network receives an aerial image and a desired class, and makes a yes or no output by matching the image and the textual description of the desired class. We train and test our model on a synthetic aerial dataset and our desired classes consist of the combination of the class types and colors of the vehicles. This strategy helps when considering more classes in testing than in training.

Keywords

aerial vehicle recognition convolutional neural network text-guided classifier synthetic dataset multi-class generalization

Cite

@article{arxiv.1808.08560,
  title  = {Convolutional Neural Networks for Aerial Vehicle Detection and Recognition},
  author = {Amir Soleimani and Nasser M. Nasrabadi and Elias Griffith and Jason Ralph and Simon Maskell},
  journal= {arXiv preprint arXiv:1808.08560},
  year   = {2018}
}

Comments

This paper has been accepted in the National Aerospace Electronics Conference (NAECON) 2018 and would be indexed in IEEE

Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

Abstract

Keywords

Cite

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