Object classification with convolution neural network based on the time-frequency representation of their echo

Abstract

This paper presents classification of spherical objects with different physical properties. The classification is based on the energy distribution in wideband pulses that have been scattered from objects. The echo is represented in Time-Frequency Domain (TFD), using Short Time Fourier Transform (STFT) with different window lengths, and is fed into a Convolution Neural Network (CNN) for classification. The results for different window lengths are analysed to study the influence of time and frequency resolution in classification. The CNN performs the best results with accuracy of (98.44 ± 0.8)% over 5 object classes trained on grayscale TFD images with 0.1 ms window length of STFT. The CNN is compared with a Multilayer Perceptron classifier, Support Vector Machine, and Gradient Boosting.