Cylinder sleeve defect mark detection method based on deep learning

A detection method and deep learning technology, applied in the field of image processing, can solve problems such as slow detection time, achieve the effect of improving accuracy, reducing search time, and enhancing detection effect

Pending Publication Date: 2022-05-27
NANJING INST OF TECH
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Problems solved by technology

The effect of this is better than the accuracy of the first-order network, but the detection time is slower

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  • Cylinder sleeve defect mark detection method based on deep learning
  • Cylinder sleeve defect mark detection method based on deep learning
  • Cylinder sleeve defect mark detection method based on deep learning

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[0027] The accompanying drawings are for illustrative purposes only, and should not be construed as limitations on this patent;

[0028] In order to better illustrate this embodiment, some components in the drawings may be omitted, enlarged or reduced, which do not represent the size of the actual product; for those skilled in the art, some well-known structures and their descriptions in the drawings may be omitted. understandable. The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] Please refer to the attached figure 1 , First, the original high-resolution training image (size 2448*2048) is subjected to noise reduction processing by 5*5 Gaussian filter, and then the local defect map is intercepted according to the position of the defect calibration, and normalized to 256*256 size image ,like figure 1 In (a) stage; secondly, the visual transformer is used as the network skelet...

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Abstract

The invention provides a cylinder sleeve defect mark detection method based on deep learning, and the method comprises the following steps: collecting a cylinder sleeve image to construct an original data set, carrying out the preprocessing and marking of the images in the original data set, and obtaining a local defect graph of a training set; carrying out modeling by using a Mask R-CNN algorithm based on a Swin converter to obtain a network model; detecting the images of the test set through the obtained network model; extracting a region of interest from a detection result through a mask mechanism to enhance a detection effect; evaluating the detection performance of the network model; and performing defect mark detection on the acquired cylinder sleeve image through the obtained network model. According to the invention, through image preprocessing, cylinder liner noise influence reduction, local graph construction of an original graph, obtaining of a new batch of training data, through a method of mapping a large graph with a small graph, obtaining of a detection result of the original graph, through a mask mechanism, filtering of a noise area, and improvement of small target detection precision.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a cylinder liner defect mark detection method based on deep learning. Background technique [0002] As the core component of the internal combustion engine, the performance of the cylinder liner directly affects the overall performance of the internal combustion engine. During the production process of the cylinder liner, different defects such as blisters, cracks and wear may occur due to temperature, impurities and uneven stress distribution during processing. The detection of surface defects has become an essential link in the delivery process. The defect size of the cylinder liner is small, and the picture noise is large. According to the traditional human eye detection, the efficiency is low, the accuracy is not high, and it is easy to miss the detection phenomenon. At present, human eye detection can no longer meet the requirements of enterprises, so the use of deep learni...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06V10/774G06V10/25G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/20081G06T2207/20084G06T2207/20104G06T2207/30164G06N3/045G06F18/214Y02P90/30
Inventor 黄晓华邵秀燕郝飞刘迁
Owner NANJING INST OF TECH
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