Method for generating deep learning samples

Abstract

The invention belongs to the technical field of image processing and discloses a method for generating deep learning samples. The invention comprises the following steps: acquiring an initial image taken under a pure color background; acquiring position and contour of a target image from the initial image, and intercepting the target image; performing data augmentation on the intercepted target image so as to obtain an initial data set of the target image; placing the current target image stochastically in a preset background image and performing Poisson image fusion after an arbitrary targetimage is selected from the initial data set, and recording the position of the current target image in the preset background image; repeating the previous step so as to form a target detection data set of deep learning. The invention reduces the preparation time cost, the labor cost and the hardware cost of the massive target data, generates a high-quality target detection data set, further provides a high-quality sample for deep learning, and increases the robustness of a target detection network.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for generating deep learning samples. Background technique [0002] In the multi-target detection tasks of image recognition, such as commodity recognition, signboard recognition, etc., deep learning methods are often used, which require a large amount of training sample data, and need to mark the targets in the image, but often collect data and mark data High cost is required. [0003] At the same time, generally when the training sample set is small, data augmentation techniques are used, that is, operations such as rotating, cropping, and flipping the training image are used to expand the sample data set. However, this processing is too simple and does not increase the complexity of the background. The degree, so the application in the target detection task, the effect is not good. Contents of the invention [0004] In order to solve the above...

AI Technical Summary

Problems solved by technology

[0002] In the multi-target detection tasks of image recognition, such as commodity recognition, signboard recognition, etc., deep learning methods are often used, which require a large amount of training sample data, and need to mark the targets in the image, but often collect data and mark data require high cost

[0003] At the same time, generally when the training sample set is small, data augmentation techniques are used, that is, operations such as rotating, cropping, and flipping the training image are used to expand the sample data set. However, this processing is too simple and does not increase the complexity of the background. degree, so the application in the target detection task, the effect is not good

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