Aluminum profile defect detection method

A defect detection and aluminum profile technology, applied in neural learning methods, biological neural network models, image data processing, etc., can solve the problems of inability to generalize aluminum profiles, poor generalization effect of defects, etc., achieve accurate regression positioning, and enhance generalization. high-precision effect

Pending Publication Date: 2021-06-22
WUHAN TEXTILE UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides a defect detection method for aluminum profiles to solve the problem that the existing defect detection methods cannot obtain better generalization on aluminum profiles, especially the generalization effect for different types of defects is poor The problem

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  • Aluminum profile defect detection method
  • Aluminum profile defect detection method

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Embodiment 1

[0056] Embodiments of the present invention provide a method for detecting defects in aluminum profiles, such as figure 1 shown, including:

[0057] Step 1, use the aluminum profile surface defect detection model to detect the surface of the aluminum profile.

[0058] Step 2: If a defect is detected, category judgment and regression positioning are performed on the defect.

[0059] Among them, the aluminum profile surface defect detection model uses Resnet-101 as the backbone network; the aluminum profile surface defect detection model adopts the feature pyramid network and deformation convolution algorithm for targeted detection of irregular defects on the aluminum profile surface; The defect detection model uses the region of interest stacking (ROI Align) extraction feature map algorithm and the improved candidate frame generation network loss function to locate small defects.

[0060] In this embodiment, the aluminum profile surface defect detection model adopts Resnet-10...

Embodiment 2

[0105] The environment used for the detection model training in this embodiment is configured as Intel i7-9700K processor, 64GB memory, two NVIDIA GeForce RTX 2080Ti graphics cards, the used deep learning framework is Pytorch, the value is 0.5, and the training epochs is 100. During the training process The accuracy curve (Accuracy) and the loss curve (LOSS) such as Image 6 shown. After the model is trained, some detection effects on the test set are as follows: Figure 7 shown.

[0106] In order to test the ability of the improved model in this paper to detect and identify different types of defects in aluminum profiles, firstly, 10 types of defects in aluminum profiles, such as dirty spots, bumps, and pits, were selected for verification. The results are shown in Table 1.

[0107] Table 1 Model performance of different defect types

[0108]

[0109] By analyzing the data in the table, it can be concluded that the detection of different types of defects has high accura...

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Abstract

The invention discloses an aluminum profile defect detection method. The method comprises the following steps: detecting the surface of an aluminum profile by adopting an aluminum profile surface defect detection model; if flaws are detected, performing category judgment and regression positioning on the flaws, wherein the aluminum profile surface defect detection model takes Resnet-101 as a main network; the aluminum profile surface flaw detection model adopts a feature pyramid network and a deformation convolution algorithm and is used for performing targeted detection on irregular flaws on the surface of an aluminum profile; according to the aluminum profile surface defect detection model, an ROI Align feature map extraction algorithm and an improved candidate box generation network loss function are adopted to position tiny defects. A feature pyramid network model is improved through a deformation convolution algorithm, so that the detection model has higher matching capability for irregular flaw features; and by improving a candidate box generation network loss function, the regression positioning of the detection model on the tiny flaws is more accurate.

Description

technical field [0001] The invention relates to the technical field of image detection, in particular to a method for detecting defects of aluminum profiles. Background technique [0002] In non-standard design, aluminum profiles are often used. After the surface of industrial aluminum profiles is oxidized, the appearance is very beautiful. When assembling into products, special aluminum profile accessories are used, which does not require welding, which is more environmentally friendly, and can be installed, disassembled, carried, and moved. Extremely convenient. [0003] The production of aluminum profiles requires product qualification inspection. For example, for surface defects of objects, the traditional detection method adopts manual inspection, which is not only time-consuming and labor-intensive, but also causes leakage due to the small distinction between aluminum profile lines and defects, and manual inspection is easy to fatigue. High detection rate and low dete...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06N3/04G06N3/08
CPCG06T7/0004G06T5/002G06N3/084G06T2207/20104G06T2207/30136G06N3/045
Inventor 罗维平周博
Owner WUHAN TEXTILE UNIV
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