Large-scale commodity identification method based on deep learning

A technology of deep learning and recognition methods, applied in the field of image recognition, can solve the problems of high equipment requirements, slow operation speed, large workload, etc., to achieve the effect of reducing training difficulty, stable recognition ability, and improving model accuracy

Pending Publication Date: 2019-11-15
广州众聚智能科技有限公司
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AI Technical Summary

Problems solved by technology

[0012] In order to solve the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide a large-scale product recognition method based on deep learning, which is used to solve the problem that the accuracy of the prior art is diffic

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  • Large-scale commodity identification method based on deep learning

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Embodiment 1

[0051] Such as figure 1 As shown, a large-scale product recognition method based on deep learning includes the following steps:

[0052] S1: Establish a product detection model, the specific method includes the following steps:

[0053] S1-1: Perform data enhancement processing on the existing multi-scenario and multi-commodity dataset, which includes pictures of various commodities under different backgrounds and lighting environments;

[0054] Divide the multi-scenario and multi-commodity data set into a detection network training set and a detection network test set. The detection network training set includes no less than 90,000 training pictures, and the training pictures include no less than 1,300 types of goods and scene data;

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Abstract

The invention belongs to the technical field of image recognition, and discloses a large-scale commodity recognition method based on deep learning, and the method comprises the following steps: S1, building a commodity detection model; S2, inputting a to-be-detected picture into the commodity detection model, and obtaining all commodity positioning data in the to-be-detected picture; S3, establishing a commodity classification model; and S4, inputting the to-be-detected picture into the commodity classification model, and obtaining corresponding commodity category data according to all the commodity positioning data. The method solves the problems that in the prior art, the precision is difficult to meet the requirements of large-scale projects, the requirements of training samples are extremely high, rapid iterative updating cannot be realized, the reusability of the samples is low, the equipment requirements are high, the feature expression capability is limited, the workload is hugeand the operation speed is low.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to a large-scale commodity recognition method based on deep learning. Background technique [0002] The automatic settlement of goods is mainly based on the information in the picture, using the target detection method to extract the information in the image and detect the list of goods contained in the image. In general, it is necessary to be able to detect and recognize multi-commodity images (the images contain multiple different commodities), and finally obtain a list of commodities in the images. The target detection task not only needs to identify what objects are in the static image, what category it is, but also needs to predict the location of the object. In the field of object detection, target detection or target segmentation methods are commonly used to locate and classify object positions in one module, and finally identify all target objects in s...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/10G06F18/241
Inventor 孙永海周敏仪徐清侠周斌卢炬康
Owner 广州众聚智能科技有限公司
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