In this paper, we constructed a one-dimensional convolutional neural network as a classifier model for spatial object classification. Considering that there are few available training samples obtained from actual measurement, combining with the characteristics of actual measurement data, we simulated a large amount of data for training and testing. The simulation results show that our method has a high classification accuracy and can overcome the problems existing in actual measurement, such as tracking mixed batches to a certain extent, and it can also effectively solve the problem that it is difficult to directly train neural networks because of the small number of spatial target samples, which take advantage of neural network autonomous learning and memory to reliably identify features.
KEYWORDS: Infrared imaging, Infrared radiation, Convolution, Computer simulations, Image segmentation, Signal to noise ratio, RGB color model, Motion models, Data processing, Data modeling
The fully convolution network is a very powerful visual model that can be used to extract features in an image. We improved a network model that can be used for end-to-end, pixel-to-pixel training to extract target motion trajectories in infrared images. The dataset used in our training comes from the simulation dataset produced by the public infrared dataset combined with the simulation trajectory. In order to enhance the model’s robustness, we add the pepper and salt noise and white noise to the simulated image, and use image augmentation to increase the number of the image. We achieved highly train and test accuracy in our simulation dataset.
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