Image classification method based on field programmable gate array (FPGA)

A classification method and gate array technology, applied in the computer field, can solve problems such as low detection accuracy, poor natural image classification effect, and limited application range, and achieve the effects of enhancing application range, improving accuracy, and ensuring classification speed

Inactive Publication Date: 2018-11-16
XIDIAN UNIV
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Problems solved by technology

However, this method still has shortcomings in that it uses image score spectrum and pixel features to classify images. Although it is suitable for the classification of SAR images, it is not effective for opti

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  • Image classification method based on field programmable gate array (FPGA)
  • Image classification method based on field programmable gate array (FPGA)
  • Image classification method based on field programmable gate array (FPGA)

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Embodiment Construction

[0026] Attached below figure 1 The specific steps of the present invention are further described in detail.

[0027] Step 1, get the test data set.

[0028] 132 pictures are randomly selected from each category of the picture set containing 4 target categories to form the test data set.

[0029] Step 2, get the training data set.

[0030] 220 pictures are randomly selected from each category of the picture set containing 4 target categories to form the training data set.

[0031] Step 3, build a convolutional neural network.

[0032] Build a convolutional neural network with 15 layers including 10 convolutional layers, 3 maximum pooling layers, 1 average pooling layer and a softmax layer.

[0033] The structure of the 15-layer convolutional neural network is as follows: the 1st, 3rd, 5th, 6th, 7th, 8th, 9th, 10th, 11th, and 13th layers of the network are convolutional layers, and the 2nd, 4th, and 12th layers are the largest Pooling layer, the 14th layer is the average po...

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Abstract

The invention discloses an image classification method based on a field programmable gate array (FPGA). The method comprises the realization steps of (1), obtaining a test data set; (2), obtaining a training data set; (3), establishing a 15-layer convolutional neural network comprising 10 convolutional layers, 3 max-pooling layers, an average-pooling layer and a soft max layer; (4), setting kernelparameters of each layer in the convolutional neural network; (5), training the convolutional neural network through utilization of the training data set; (6), inputting test images into the convolutional neural network, and classifying the test data set; and (7), computing accuracy of the test data set. The method can be used for classifying optical images, SAR (Synthetic Aperture Radar) imagesand natural images on the field programmable gate array (FPGA).

Description

technical field [0001] The invention belongs to the field of computer technology, and further relates to an image classification method based on Field Programmable Gate Array FPGA (Field Programmable GateArray) in the field of deep learning technology. The present invention can accelerate the image classification method based on the deep convolutional neural network, and can realize the classification of synthetic aperture radar SAR (Synthetic Aperture Radar) images, optical images and natural images on the FPGA. Background technique [0002] The SAR image classification method with high speed and high performance is the core technology in the field of computer vision. In recent years, deep convolutional neural networks have made great achievements in the field of computer vision. Compared with traditional methods, image classification algorithms based on deep convolutional neural networks have significantly improved classification accuracy. For image classification of comp...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045G06F18/2414G06F18/214
Inventor 田小林逯甜甜张晰焦李成丁鑫
Owner XIDIAN UNIV
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