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workpiece recognition device and method based on an image recognition-SVM learning model

A learning model and image recognition technology, applied in the field of image recognition, can solve problems such as lack of active perception of changes in the working environment and adaptability, poor generalization performance of recognition algorithms, failure of robot recognition or grasping, etc., to achieve recognition accuracy and generalization Performance improvement, high stability and real-time performance, and the effect of improving the robustness of recognition

Active Publication Date: 2019-04-19
FUJIAN INST OF RES ON THE STRUCTURE OF MATTER CHINESE ACAD OF SCI
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the generalization performance of the recognition algorithm is poor, and it lacks the ability to actively perceive changes in the working environment and adapt to changes, resulting in the failure of robot recognition or grasping

Method used

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  • workpiece recognition device and method based on an image recognition-SVM learning model
  • workpiece recognition device and method based on an image recognition-SVM learning model
  • workpiece recognition device and method based on an image recognition-SVM learning model

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Embodiment Construction

[0052] The present application is described in detail below in conjunction with the examples, but the present application is not limited to these examples.

[0053] see figure 2 The workpiece recognition and grasping device based on the image recognition-SVM learning model provided by the present application includes: an image acquisition unit, a recognition unit and a robot, an image acquisition unit, a recognition unit and a robot, and the image acquisition unit is used to obtain the described The image of the workpiece to be detected is connected with the data of the recognition unit; the recognition unit is used to extract the feature vector of the workpiece in the image, and then use the SVM learning model classifier to classify the workpiece to be detected, and output the classification result, It is connected with the control of the robot; the robot is used to classify the workpieces to be detected according to the classification results.

[0054] In this application,...

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Abstract

The invention discloses a workpiece recognition device and method based on an image recognition-SVM learning model. The device comprise an image collection unit, a recognition unit and a robot, and the image collection unit is used for obtaining a to-be-detected workpiece image and is in data connection with the recognition unit; The recognition unit is used for classifying the workpieces to be detected by adopting an SVM learning model classifier after extracting the feature vectors of the workpieces in the images, outputting a classification result and being in control connection with the robot; And the robot is used for classifying the workpieces to be detected according to the classification result. The device can identify and grab a predetermined target in a relatively complex environment, is not influenced by translation, scale and rotation geometric changes, and has relatively high stability and real-time performance. Another aspect of the application also provides a method of the device.

Description

technical field [0001] The present application relates to a workpiece recognition device and method based on an image recognition-SVM learning model, belonging to the field of image recognition. Background technique [0002] With the continuous development of robot technology and artificial intelligence, machine vision has been widely used in various fields such as industrial inspection, sorting, and production automation. In recent years, related machine vision recognition algorithms have been widely used in workpiece recognition and automatic sorting of assembly lines. Most of the existing machine vision recognition algorithms are recognized through the traditional matching cost function and the unique features of the workpiece, such as grayscale, corner points, geometric primitives, etc., but the generalization performance of the recognition algorithm is poor, and they lack active perception of changes in the working environment and The ability to adapt to changing circu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06T7/00G06T7/12G06T7/136
CPCG06T7/0004G06T7/12G06T7/136G06T2207/30164G06T2207/10004G06T2207/20081G06V10/507G06F18/2411
Inventor 杨林杰李俊崇米娜
Owner FUJIAN INST OF RES ON THE STRUCTURE OF MATTER CHINESE ACAD OF SCI
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