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Part defect detection and positioning method based on deep learning and normal graphs

A deep learning and defect detection technology, applied in the field of visual inspection, can solve problems such as part defect detection

Inactive Publication Date: 2018-08-10
NANJING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides a part defect detection and localization method based on deep learning and normal graph, which can solve the problem of part defect detection that cannot be solved by traditional computer vision

Method used

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  • Part defect detection and positioning method based on deep learning and normal graphs
  • Part defect detection and positioning method based on deep learning and normal graphs
  • Part defect detection and positioning method based on deep learning and normal graphs

Examples

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Embodiment 1

[0082] refer to figure 1 As shown, a method of component defect detection and localization based on deep learning and normal graph, specifically includes the following steps:

[0083] Step 1: Acquire the original image and calculate the normal map.

[0084] Step 2: Mesh the image.

[0085] Step 3: Select different defect images and normal part images to train the model.

[0086] Step 4: Collect the surface information of the part to be detected and calculate the normal map.

[0087] Step 5: Mesh the image.

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Abstract

The invention discloses a part defect detection and positioning method based on deep learning and normal graphs. The method comprises the steps that 1, an original image is collected, and a material surface normal graph is obtained through calculation; 2, meshing is performed on the surface normal graph; 3, normal graphs obtained after division of parts with different defects and normal graphs obtained after division of normal parts are used to train a model; 4, an image of a to-be-detected part is collected, and a material surface normal graph is calculated; 5, meshing is performed on the normal graph of the to-be-detected part; 6, images obtained after division in the step 5 are used as input to perform defect detection according to the trained model obtained in the step 3; and 7, feedback and defect positioning are performed according to the detection result in the step 6 and the division result in the step 5.

Description

technical field [0001] The invention belongs to the technical field of visual inspection, and relates to a method for detecting and locating parts defects based on deep learning and normal graphs. Background technique [0002] With the development of industry, the demand and growth of metal parts have increased significantly. In industrial production, the processing of metal parts has basically fully realized the automatic mechanical production. In practical applications, the requirements for metal parts are often very high, especially for precision instruments such as automotive core components, which often require that the surface should not have defects with a depth or width exceeding 5mm. However, in the process of parts processing, due to the problems of its own equipment, or environmental factors in the process of processing and other factors, various defects will inevitably appear, such as cracks, peeling, pulling lines, scratches, pits, protrusions , spots, corrosi...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/90G06N3/04
CPCG06T7/0004G06T7/90G06T2207/10024G06T2207/20081G06T2207/10004G06T2207/30164G06N3/045
Inventor 宋佳张扬郭延文
Owner NANJING UNIV
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