Weld seam and weld seam defect detection method based on deep learning

A defect detection and deep learning technology, which is applied in the direction of optical test defects/defects, can solve the problem that the type and position of weld defects cannot be effectively detected, and achieve the effect of increasing accuracy and classifying accurately

Active Publication Date: 2019-06-18
易思维(杭州)科技有限公司
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Problems solved by technology

[0004] During the welding seam detection process, due to problems such as image deformation, image shooting quality, and angle, the existing two-dimensional image detection methods such as template matching cannot effectively detect the specific defect types and locations in the weld seam; in order to solve the above probl

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  • Weld seam and weld seam defect detection method based on deep learning
  • Weld seam and weld seam defect detection method based on deep learning
  • Weld seam and weld seam defect detection method based on deep learning

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Embodiment Construction

[0042] The technical solution of the present invention will be described in detail below in conjunction with the drawings and embodiments.

[0043] A method for detecting welds and weld defects based on deep learning, using YOLO V3 network to achieve weld and / or weld defect detection; (YOLO V3 network includes input layer, interconnected convolutional layer, activation function layer, dropout Layer, residual layer, fully connected layer, softmax logic output layer; between two adjacent layers, the output value of the previous level is used as the input value of the next level.)

[0044] The YOLO V3 network for weld and / or weld defect detection is trained through the following steps:

[0045] 1) Use the rectangular positioning frame to select and mark the welds of the workpiece images containing the welds, and use 5000 such images as the training data set;

[0046] As an embodiment of the present invention, a single workpiece image contains 2-15 weld seam areas;

[0047] The weld image ...

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Abstract

The invention discloses a weld seam and weld seam defect detection method based on deep learning. A YOLOV3 network is used to realize weld seam and/or weld seam defect detection. A network training step comprises the following steps of using a positioning frame to select and mark the weld seams in a workpiece image and taking as a training data set; using the positioning frame to select weld defects and mark defect types in a weld seam image, and taking as a training data set I; acquiring coordinates xp and yp of the positioning frame, and width and height sizes wp and hp; initializing a network; randomly acquiring an input tensor aj for training calculation, and outputting a detection result; calculating an error function loss of a prediction result by using the detection result; and adjusting a weight W and an offset value b in combination with a gradient descent method, repeating in the way, and acquiring a trained network. The multiple weld seams and the multiple defect types can be synchronously detected, weld seam identification positioning and defect detection can be realized in one measurement, and measurement efficiency and accuracy are effectively increased.

Description

Technical field [0001] The invention relates to the field of defect detection, in particular to a welding seam and a welding seam defect detection method based on deep learning. Background technique [0002] With the development of automation technology, industrial welding robots have been widely used in the field of manufacturing and have become the main automation equipment. New industrial welding robots use remote laser welding technology to overcome the limitations of traditional welding (such as arc welding robot posture The electric welding gun is limited by the size of the workpiece), which has the advantages of fast workpiece welding and small thermal deformation. [0003] In contrast, an efficient welding seam quality inspection method is needed to match the processing cycle requirements. The conventional structured light sensor has high measurement accuracy and can measure three-dimensional parameters. Its scanning working method is suitable for multiple welding seams on ...

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Application Information

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IPC IPC(8): G01N21/88
Inventor 赵进崔鹏飞郭磊
Owner 易思维(杭州)科技有限公司
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