Workpiece detecting and sorting device and method based on visual robot

Abstract

The invention discloses a workpiece detecting and sorting device and method based on a visual robot. The device comprises a transmission device, a robot and a computer. The transmission device collects a workpiece image, analysis is carried out through a computer, and the robot grabs a damaged workpiece. Compared with the prior art, the workpiece detecting and sorting device and method based on the visual robot is characterized in that Haar-like features are adopted to establish a weak classifier based on gray level changes, an AdaBoost algorithm is used for forming a strong classifier with high accuracy, cascading of the strong classifier is then carried out, a region of interest (ROI) containing a target object is extracted in the image, and the better detection accuracy and the real-time effect are obtained; a defect image is obtained in the target object, and a level set method is used for obtaining a defect ROI to form a training sample; and a convolutional neural network (CNN) isintroduced as a training model, and the accuracy of defect classification is improved.

Description

technical field [0001] The invention relates to a device and method for detecting and sorting workpieces based on vision robots, belonging to the field of machine vision detection and classification. Background technique [0002] During the production process of the workpiece, due to its production environment, processing technology and production equipment and other factors, some surface defects will occur. For example, casting parts will cause surface defects such as holes, cracks, and air bubbles during the production process, and may also cause workpiece shape errors. . If it cannot be detected and processed in time, it will cause waste of processing resources and cause economic losses to the enterprise. Therefore, it is necessary to detect and classify the surface defects of the produced workpieces to avoid unnecessary losses caused by unqualified workpieces flowing into the market. The traditional detection method is mainly done manually and the detection accuracy is ...

AI Technical Summary

Problems solved by technology

[0003] In view of the fact that there are many types of surface defects on the workpiece, most of the defects are relatively small, and the resolution of the defect image in the two-dimensional image is relatively low, and it will be disturbed by oil, dust, etc.

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