Steel structure corrosion identification method based on convolutional neural network

A technology of convolutional neural network and identification method, which is applied in the field of steel structure corrosion identification based on convolutional neural network, can solve the problems of poor universality of manual design, and achieve the effects of improving efficiency, high identification accuracy, and strong generalization performance

Pending Publication Date: 2019-09-13
TIANJIN UNIV
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Moreover, the universality of artificially designed features is poor

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  • Steel structure corrosion identification method based on convolutional neural network
  • Steel structure corrosion identification method based on convolutional neural network
  • Steel structure corrosion identification method based on convolutional neural network

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[0034] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0035] Such as figure 1 Shown, is the flow chart of the steel structure corrosion degree identification based on convolutional neural network of the present invention, comprises the following steps:

[0036] Step 1: Collect steel structure corrosion pictures and establish a steel structure corrosion picture data set, divide training set and test set;

[0037] Step 2: Design the convolutional neural network model architecture according to the dataset size, image size, and number of image categories;

[0038] Step 3: Perform hyperparameter optimization through cross-validation, use the training set data to train the convolutional neural network model, and obtain the convolutional neur...

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Abstract

The invention discloses a steel structure corrosion identification method based on a convolutional neural network. The steel structure corrosion identification method comprises the steps: 1, collecting pictures of a steel structure, preprocessing the pictures, and dividing a training set and a test set; 2, designing a convolutional neural network structure; 3, performing hyper-parameter optimization and model training through cross validation; and 4, inputting the to-be-identified pictures of the steel structure into the model obtained in the step 3 to obtain a rust identification result. According to the steel structure corrosion identification method, the convolutional neural network is utilized to realize automatic extraction of the structure corrosion characteristics; complex and tedious characteristic design work is avoided; the steel structure corrosion identification efficiency is improved; accurate identification of the steel structure corrosion is achieved; an objective identification result can be provided; and a new solution is provided for identification of the steel structure corrosion.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a method for recognizing steel structure corrosion based on a convolutional neural network. Background technique [0002] Corrosion is a frequent defect in steel structures. When the components are corroded, the mechanical properties of the components will be degraded, and the bearing capacity of the structure will be reduced accordingly. Periodic corrosion inspection of the structure helps us understand the health status of the structure and take timely rust removal measures to ensure the safety of the structure. [0003] Manual-based visual inspection is currently the most commonly used corrosion detection method. Although it is easy to implement, the detection efficiency is low and the detection cost is high. Machine vision-based methods can efficiently perform rust image recognition. However, the recognition accuracy of traditio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01N21/88
CPCG01N21/8851G01N2021/8887G06V20/00G06F18/214
Inventor 徐杰桂常清韩庆华
Owner TIANJIN UNIV
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