A Target Detection Algorithm of Neural Network Based on Histogram Statistics

Abstract

Aiming at the problems of poor adaptability of traditional target detection algorithms and high computational resources of deep learning algorithms, a BP neural network target detection algorithm based on histogram statistics is proposed. It is based on the principle that similar areas have similar histograms. In this algorithm, the two-dimensional image information converts to the one-dimensional histogram information. We establish a three-layer neural network model, and the histogram is used as the input of the BP neural network. Compared to the traditional target detection algorithms, its complexity is low, and its efficiency and accuracy is high. The experimental results show that the fewer classification categories, the higher target detection probability. The computational complexity of the BP neural network is low, so the computational efficiency is quite high. The accuracy of target recognition is higher than 97% with SAR and optical images.