Construction method of natural scene data set suitable for machine vision

Abstract

The invention discloses a construction method of a natural scene data set suitable for machine vision. The construction method comprises the following steps: determining a classification category of the data set based on an ecosystem type; downloading an original image from the Internet by using the keyword; carrying out preliminary matching judgment on the downloaded images, and carrying out secondary classification on the fuzzy category images to form an image data set; and verifying the data set by using a convolutional neural network, and combining the ambiguous categories according to theconfusion matrix. The problem that natural scene classification is difficult to perform accurate and objective definition like object classification is solved, reliable data support is provided for training a deep convolutional neural network, and the method can be used for a natural scene recognition system.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for constructing a natural scene data set suitable for machine vision. Background technique [0002] In the field of image processing technology, image recognition has received more and more attention in recent years. Image recognition technology is the basis of practical technologies such as motion analysis, stereo vision, and data fusion. Among them, target recognition is due to convolutional neural networks and large-scale data. The application of the set has greatly improved the recognition accuracy, but it is difficult to obtain the same level of accuracy by using the convolutional neural network to recognize the scene where the image occurs. A large part of the reason is that the scene data set caused by inaccurate self-categorization. [0003] Using machine vision to identify objects in an image can provide an overview of what is happening in the image, ...

AI Technical Summary

Problems solved by technology

[0004] For most scene databases, there are often the following two problems: 1) The hierarchy between categories is confusing, and there is confusion between the categories of "farmland" and "plain" in some datasets, where plain is a landform type , and farmland can also be established on the plains, and the two are not independent; 2) The meaning is repeated and confused, such as "forest" and "forest" are only distinguished under special circumstances

[0005] Different from the objective evaluation of category labels in the construction process of object data sets, scene data sets often have different judgment standards for the same image due to different subjective evaluations, which also leads to great differences between images in the same category. Limits the improvement of algorithm accuracy, so it is necessary to establish a unified classification criterion in the scene data set

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