Garbage recognition evolutionary learning method, device, system and medium based on deep learning

A deep learning and garbage technology, applied in the fields of trash cans, garbage collection, character and pattern recognition, etc., can solve the problems of reducing the workload of maintenance personnel, the limited amount of garbage identified, and the lack of accuracy, so as to reduce the workload and improve the accuracy. And the effect of breadth and strong real-time

Active Publication Date: 2021-08-31
GUANGZHOU UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The first object of the present invention is to provide a garbage recognition evolutionary learning method based on deep learning, which improves the current garbage recognition technology, and at the same time uses the data with poor recognition effect as the evolutionary power to update and improve the trained garbage recognition model in real time, Make the trained garbage recognition model have the real-time and accuracy that other garbage collection devices do not have, and greatly reduce the workload of maintenance personnel, realize the accurate classification of a large amount of garbage, and solve the problem that the current garbage recognition device recognizes the limited amount of garbage and the lack of accuracy At the same time, in the process of work, continuous self-updating and optimization

Method used

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  • Garbage recognition evolutionary learning method, device, system and medium based on deep learning
  • Garbage recognition evolutionary learning method, device, system and medium based on deep learning
  • Garbage recognition evolutionary learning method, device, system and medium based on deep learning

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Embodiment 1

[0058] Machine learning is widely used in the field of object recognition, but most of its data sets rely on manpower, resulting in the trained model is always flawed, requiring high-frequency maintenance work by maintenance personnel. Therefore, this embodiment provides a garbage recognition evolutionary learning system based on deep learning. The system considers that in the field of garbage recognition, the types of garbage are changing with each passing day, and the data features that need to be extracted when the computer recognizes the target are also changeable, so the evolutionary learning system is proposed. The mechanism uses the data with poor recognition effect as the evolutionary power to update and improve the computer training model in real time to adapt to the changeable characteristics of the recognized garbage, and greatly improve the accuracy and breadth of recognition on the basis of existing technologies.

[0059] like figure 1 and figure 2 As shown, the...

Embodiment 2

[0077] like Figure 9 As shown, this embodiment implements the data processing of the information processor in the above-mentioned embodiment 1 through the server, that is, the server is directly connected to the camera and the controller. This embodiment provides a garbage recognition evolutionary learning method based on deep learning. The method Applied to the server, including the following steps:

[0078] S901. Obtain garbage sample image data.

[0079] S902. Perform preprocessing on the garbage sample image data, specifically: demeaning, normalization, PCA and whitening processing on the garbage sample image data.

[0080] S903. Using the preprocessed garbage sample image data as an input parameter of the neural network, compare it with the trained garbage recognition model, and judge whether the recognition is successful according to the comparison result, specifically:

[0081] The preprocessed garbage sample image data is used as the input parameter of the neural ne...

Embodiment 3

[0089] like Figure 10 As shown, this embodiment provides a garbage recognition evolutionary learning device based on deep learning, which includes an acquisition module 1001, a preprocessing module 1002, a first recognition module 1003, a feedback module 1004, a second recognition module 1005, a first The update module 1006 and the second update module 1007, the specific functions of each module are as follows:

[0090] The acquiring module 1001 is configured to acquire garbage sample image data.

[0091] The preprocessing module 1002 is used for preprocessing the garbage sample image data, specifically: for removing the mean value, normalizing, PCA and whitening the garbage sample image data.

[0092] The first recognition module 1003 is used to compare the preprocessed garbage sample image data with the trained garbage recognition model as the input parameter of the neural network, and judge whether the recognition is successful according to the comparison result, specific...

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Abstract

The invention discloses a garbage recognition evolutionary learning method, device, system and medium based on deep learning. The method includes: acquiring garbage sample image data; preprocessing the garbage sample image data; preprocessing the garbage sample image The data is used as the input parameter of the neural network, compared with the trained garbage recognition model, and according to the comparison result, it is judged whether the recognition is successful; the corresponding information of the successfully recognized garbage is fed back to the garbage sorting agency; the ResNet algorithm is used to identify the failed garbage The sample image data is identified again, the garbage sample image data successfully identified by the ResNet algorithm is marked, and the corresponding garbage information is fed back to the garbage classification and placement agency to update the garbage identification model; the garbage sample image data successfully identified by the ResNet algorithm is transmitted to the user Or maintainers to mark and update the garbage recognition model. The invention greatly reduces the workload of maintenance personnel and realizes accurate classification of a large amount of garbage.

Description

technical field [0001] The invention relates to a garbage identification method, in particular to a garbage identification evolutionary learning method, device, system and storage medium based on deep learning, belonging to the technical field of garbage identification. Background technique [0002] The origin of machine learning can be traced back a long time ago, and deep learning is also within the category of machine learning. As a new field in the field of machine learning, it appeared in 2006 and its development momentum has not diminished in recent years. The structure of deep learning is compared with the traditional shallow learning institutions. Compared with the shallow structure, the structure of deep learning is more complicated, and the number of training layers of the neural network involved is more, so it is more complex than the traditional shallow structure. The learning structure can learn more abstract features of the data, and it is more sensitive to the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62B65F1/00B65F1/10
CPCB65F1/0033B65F1/10B65F2001/008G06F18/217G06F18/241G06F18/214Y02W30/10
Inventor 杨兴鑫刘长红彭绍湖张宏康李文杰朱亮宇钟志鹏程健翔范俊宇黄楠陈建堂
Owner GUANGZHOU UNIVERSITY
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