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

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...

AI Technical Summary

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 optical images and natural images, which greatly limits its application. scope

The disadvantage of this method is that due to the use of support vector machine SVM to classify images, the detection accuracy is low

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