Casting surface defect identification method based on deep convolutional neural network

A convolutional neural network and defect identification technology, applied in the field of casting surface defect detection, can solve problems such as inaccurate positioning, easy misjudgment and missed judgment, and inability to accurately predict size, achieving high calculation efficiency, strong classification ability, and improved The effect of target discrimination ability

Active Publication Date: 2020-06-02
SOUTHEAST UNIV
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Problems solved by technology

However, most of these methods are used for object recognition in nature, and when they are applied to casting surface defects, there are inaccurate positioning, inability to accurately predict the size, and easy misjudgment and missed targets.

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  • Casting surface defect identification method based on deep convolutional neural network
  • Casting surface defect identification method based on deep convolutional neural network
  • Casting surface defect identification method based on deep convolutional neural network

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Embodiment Construction

[0044]The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0045] The invention proposes a casting surface defect recognition method based on a deep convolutional neural network, which can detect casting surface defects intelligently and online in real time. This method first uses the designed SCN with a symmetric module to extract features from the input image, then uses three prediction branches similar to the feature pyramid network to make predictions on three scales based on the features extracted by the backbone network, and finally passes the non-maximum value The suppression algorithm (NMS) screens the target boxes.

[0046] The flow chart of the casting surface defect recognition method based on deep convolutional neu...

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Abstract

The invention discloses a casting surface defect identification method based on a deep convolutional neural network. The method comprises the following steps: 1, collecting a casting surface defect image, marking the image, and establishing a data set of common casting surface defects; 2, constructing a deep convolutional neural network defect recognition model; 3, constructing a network loss function; 4, dividing the data set into a training set and a test set, and training the defect recognition network by using the training set; 5, inputting the test image into the trained network to identify the position, the type and the size of the defect. According to the invention, the recognition precision and recognition performance of the casting surface defects are improved, and the online, intelligent and automatic development of casting quality detection is promoted.

Description

technical field [0001] The invention belongs to the field of casting surface defect detection, and in particular relates to a casting surface defect recognition method based on a convolutional neural network. Background technique [0002] Castings are used in a wide range of fields, however, due to some problems in raw materials or casting process, castings will have defects. Surface defects are a large part of casting defects. Surface defects in castings will affect the appearance of products, reduce the strength of materials, shorten product life and increase safety-related risks. Therefore, it is very important to identify surface defects of castings. [0003] The method of workpiece surface defect identification has been developed for many years. In addition to manual inspection, traditional methods mainly include eddy current testing, magnetic flux leakage testing, etc. The key issue is how to achieve real-time, intelligent and effective detection while reducing person...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0004G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30116Y02P90/30
Inventor 贾民平邢俊杰黄鹏胡建中许飞云
Owner SOUTHEAST UNIV
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