Rapid target detection method based on convolutional neural network

A convolutional neural network, target detection technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve the problem that the overall framework detection effect is not very outstanding, the model efficiency is not very high, etc.

Active Publication Date: 2015-04-29
XIAMEN UNIV
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Problems solved by technology

However, this method of separately performing the classification of the convolutional neural network and the target detection in the defo

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  • Rapid target detection method based on convolutional neural network
  • Rapid target detection method based on convolutional neural network
  • Rapid target detection method based on convolutional neural network

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Embodiment Construction

[0062] Below in conjunction with accompanying drawing and embodiment the method of the present invention is described in detail, present embodiment is implemented under the premise of technical solution of the present invention, has provided embodiment and specific operation process, but protection scope of the present invention is not limited to following the embodiment.

[0063] see figure 1 , the embodiment of the present invention includes the following steps:

[0064] A. Prepare the training sample set (x i ,y i ), i=1,..., N, N is the number of training samples, and N is a natural number. x i Indicates the fixed-size image corresponding to the training sample, the image that contains the target and the target fills the frame is a positive sample, and the other images are negative samples. the y i Represent a sample category vector:

[0065]

[0066]B. Divide all training samples into m batches, put m-2 batches of samples into a well-designed convolutional neura...

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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...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 王菡子郭冠军严严
Owner XIAMEN UNIV
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