Columnar diode surface defect detection device based on machine vision

Abstract

The invention discloses hardware and software algorithm designs of a columnar diode surface defect detection device. The hardware design comprises the steps of industrial camera selection, lens selection and optical platform establishment. The defect detection algorithm design of the software comprises the steps of tube body segmentation, tube body pretreatment, defective ROI segmentation, feature extraction, and decision tree classifier design. Aiming at the optical platform design, a reasonable illumination manner and a reasonable light source placement manner are tested through the optical principle and the structural properties of the object. Aiming at the design of a defect detection operator, the difficulty lies in the defective ROI segmentation and the texture feature extraction, and improved stroke width conversion and patterned gradient histogram feature extraction methods are proposed respectively; finally, the defects are classified through a decision tree classifier, the defect decision tree classifier recognition rate is close to 100%, the classification success rate reaches 96.2% and good recognition and classification results are achieved.

Description

technical field [0001] The invention relates to a hardware device and a software method for detecting surface defects of a columnar diode. Background technique [0002] At present, most of the measured objects of machine vision are planar structures with high consistency in shape and texture, such as SMT surface mount, wafer defect detection, etc. With the further improvement of the degree of automation, higher requirements are placed on the adaptability of the visual system, and more excellent feature operators and machine learning-based classifiers are required to ensure that the system has a more comprehensive and intelligent learning of complex objects. and know. [0003] For a three-dimensional structure object, the surface texture is distributed around, and the spatial position, shape feature and gray level feature of the defect have great uncertainty, and it may appear that the normal texture is mixed with the defect, and different defects are mixed, increasing the ...

AI Technical Summary

Problems solved by technology

The latter is subdivided into two types of methods, namely, image-based methods and geometry-based methods. Image-based monocular / binocular stereo vision is not mature, takes a long time, and is not suitable for real-time online detection.

Geometry-based laser scanning can obtain the depth data of the object to build a 3D model, with high accuracy and real-time performance, but the disadvantage is that the texture information is lost and the cost is high

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