Object Intelligent Detection and Implementation Based on Neural Network and Deep Learning

Abstract

Aiming at the disadvantage of high false detection rate in target classification detection using existing feature training classifiers, this paper proposes a moving target detection algorithm based on convolutional neural network on the basis of deep learning. On the image to be checked, sliding windows of different scales are used to determine whether there is a object window. In object detection, a convolutional neural network is trained with a large number of positive and negative samples. In order to better adapt to object detection, the topology of the convolutional neural network is improved. The window of suspected object is input into the improved convolutional neural network for object detection, and the false detection rate is reduced on the basis of maintaining the original detection rate.