Weakly supervised casting defect recognition method based on attention mechanism

A technology for casting defects and identification methods, applied in image data processing, instruments, analysis materials, etc., can solve the problems of complex algorithm and threshold parameter adjustment, labor consumption, increase production costs, etc., to ensure defect recognition rate and reduce labor costs. Effect

Pending Publication Date: 2020-01-03
UNIV OF SHANGHAI FOR SCI & TECH
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Problems solved by technology

Although the results of the numerical calculation method are relatively intuitive, for complex backgrounds and structures, the adjustment of the overall algorithm and threshold parameters becomes very complicated.
Although th

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  • Weakly supervised casting defect recognition method based on attention mechanism
  • Weakly supervised casting defect recognition method based on attention mechanism
  • Weakly supervised casting defect recognition method based on attention mechanism

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Embodiment Construction

[0025] Such as figure 1 The flow chart of the weakly supervised casting defect recognition method based on the attention mechanism is shown, and the method is as follows:

[0026] 1. For each acquired casting radiographic image ( figure 2 ), perform image scale normalization and color channel expansion preprocessing, and then perform multi-label weak labeling. The implementation is as follows:

[0027] 1.1. Scale each casting ray image to 512*512, and use bilinear interpolation for vacant pixels to complete the image scale normalization process;

[0028] 1.2. Extend the color channel of the image after image scale normalization processing. The expansion method is to copy the original value, and the number of channels after expansion is 3;

[0029] 1.3. Multi-type weak annotations are performed on all preprocessed images to obtain a set of all image annotations Where n is the total number of images, that is, each image also contains the type label y t and defect label y ...

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Abstract

The invention relates to a weak supervision casting defect recognition method based on an attention mechanism. The method comprises the following steps: firstly obtaining a ray image of a casting, carrying out the multi-label weak marking of the casting types and defects in the image, and forming a training set; and then training a ResNe-50 network model on the training sample set, forming an attention mechanism by utilizing multi-label information, enabling the model to accurately identify casting defects under the condition of weak supervision, and then calculating an activation mapping graph to obtain accurate positions of the defects. According to the method, under the condition that the casting defect recognition accuracy is guaranteed, the manpower consumption of labeling data is reduced, and finally the production cost is reduced.

Description

technical field [0001] The invention relates to a casting quality inspection technology, in particular to a weakly supervised casting defect recognition method based on an attention mechanism. Background technique [0002] The ray inspection system is a nondestructive inspection method to ensure the overall quality of industrial castings. It can detect internal defects of castings without damaging the castings. The ray system performs digital radiography on the target casting from different angles, and then manually observes the ray pictures according to the inspection personnel to judge whether the quality of the tested casting is qualified. Manual observation can detect internal defects of castings in radiographs based on inspection experience, but with the fatigue of inspectors, the detection efficiency and accuracy are greatly reduced. The realization of casting defect detection automation is of great significance for improving production efficiency, ensuring product qu...

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Application Information

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IPC IPC(8): G06T7/00G06T3/40G01N23/00G01N23/04
CPCG06T7/0004G06T3/4007G01N23/00G01N23/04G06T2207/20081G06T2207/20084G06T2207/30116G06T2207/30204Y02P90/30
Inventor 王永雄胡川飞
Owner UNIV OF SHANGHAI FOR SCI & TECH
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