Rapid target detection method based on convolutional neural network

Abstract

The invention relates to a rapid target detection method based on a convolutional neural network, and relates to the computer vision technology. The rapid target detection method comprises the following steps: training convolutional neural network parameters by utilizing a training set; solving the problem of max-pooling losing feature by using an expander graph and generating a discriminative complete feature graph; regarding the full-connection weight of the convolutional neural network as a linear classifier, and estimating the generalization error of the linear classifier on the discriminative complete feature by using a probable approximately correct learning framework; estimating the required number of the linear classifiers according to the generalization error and the expected generalization error threshold value; and finally, completing the target detection on the discriminative complete feature graph by using the linear classifiers on the basis of a smooth window. The detection efficiency and the target detection precision are obviously improved.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a fast target detection method based on a convolutional neural network. Background technique [0002] An important source of human perception of the world is through image information. Studies have shown that about 80% to 90% of the information that humans obtain from the outside world comes from image information obtained by human eyes. Human beings have a high ability to perceive external image information, and can quickly locate and analyze targets. In order for a computer to have powerful visual perception and understanding capabilities, it should have powerful target detection and recognition capabilities similar to humans. Object detection is an important prerequisite for visual perception and object understanding. The efficiency and accuracy of object acquisition determine the speed and effect of visual perception. Once the computer has the powerful target detection and perc...

AI Technical Summary

Problems solved by technology

However, this method of separately performing the classification of the convolutional neural network and the target detection in the deformation model makes the detection effect of the overall framework not very outstanding, and the efficiency of this model is not very high.

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