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A commodity matching and quantity regression recognition algorithm based on a preset template

A technology of regression recognition and preset templates, applied in character and pattern recognition, computing, computer parts, etc., can solve problems such as no solution

Active Publication Date: 2019-04-23
武汉市哈哈便利科技有限公司
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Problems solved by technology

There is no corresponding solution for the scene where a single object picture is matched to a multi-object picture

Method used

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  • A commodity matching and quantity regression recognition algorithm based on a preset template
  • A commodity matching and quantity regression recognition algorithm based on a preset template

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Embodiment Construction

[0022] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0023] like figure 1 , 2 As shown, this embodiment provides a product matching and quantity regression recognition algorithm based on a preset template. This algorithm is mainly modeled based on the Keras / TensorFlow deep learning framework. First, the algorithm model is trained using transfer learning technology. Once After the model training is completed, the model can be applied to match the collected source image data based on a given product template to obtain the final number o...

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Abstract

The invention discloses a commodity matching and quantity regression recognition algorithm based on a preset template. The algorithm is characterized by constructing a deep learning neural network architecture; extracting depth feature information of a commodity in the source picture; carrying out related convolution operation on the depth feature level and the feature ID of the target commodity,so that the individual number of the commodity in the picture can be recognized and returned, the scene requirement that the single-object picture is matched with the multi-object picture can be met,the target object can be recognized from the multi-object picture, and the individual number of the target object in the multi-object picture can also be recognized. The algorithm specifically comprises the following steps of collecting the training data, generating the commodity feature IDs of a commodity library, constructing a deep convolutional neural network, training the neural network, andverifying and testing a neural network model.

Description

technical field [0001] The invention belongs to the technical field of computer vision image recognition, in particular to a product matching and quantity regression recognition algorithm based on a preset template. Background technique [0002] With the substantial improvement of computer computing power and the large-scale accumulation of data, artificial intelligence technology has made breakthroughs, and all walks of life have begun to apply artificial intelligence technology to improve work efficiency and reduce business operating costs. Especially in the retail field, how to apply artificial intelligence technology to reduce operating costs and make products within easy reach, creating a new retail model has become a hot research field in the industry. At the same time, with the research achievements made by researchers in the field of computer vision in recent years, especially the use of deep learning convolutional neural network image recognition technology, the pro...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/22G06F18/253
Inventor 蔡丁丁方无迪唐开
Owner 武汉市哈哈便利科技有限公司
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