An aluminum material surface defect detection algorithm based on deep learning

A defect detection and deep learning technology, which is applied in computing, computer parts, image analysis, etc., can solve the problems of large changes in the size and shape of aluminum surface defects, unsatisfactory detection performance of aluminum surface defects, etc.

Active Publication Date: 2019-05-03
SUN YAT SEN UNIV
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Problems solved by technology

[0004]However, the detection performance of general-purpose target detection algorithms for aluminum surface defects is not nearly satisfactory....

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  • An aluminum material surface defect detection algorithm based on deep learning
  • An aluminum material surface defect detection algorithm based on deep learning
  • An aluminum material surface defect detection algorithm based on deep learning

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Embodiment Construction

[0048] The present invention is further described below.

[0049] Implementation process and examples of the present invention are as follows:

[0050] (1) Image collection, use the camera to shoot the surface of the aluminum material, obtain 5000 images and rename the images, such as 1.jpg, 2.jpg, 3.jpg, ..., 5000.jpg, etc., use the labelImg tool to shoot Annotate the image of the image to obtain the label of the defect in the image. The label of the defect includes the coordinates (x1, y1) of the upper left corner of the defect in the image, the coordinates (x2, y2) of the lower right corner of the image and the category of the defect defectN, where N represents Numbers, N ∈ {1, 2, 3, ..., 10}, respectively represent non-conductive, scratches, leaky corners, orange peel, and leaky bottoms. Jet streams, paint bubbles, pits, variegated colors, and dirty spots. In particular, if there are no defects in the captured image, we will not use labelImg to process it, and only record...

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Abstract

The invention relates to an aluminum material surface defect detection algorithm based on deep learning, and the algorithm comprises the steps: (1) employing a camera to shoot the surface of an aluminum material, obtaining a related data set, employing a labelImg tool to label an image, and obtaining label information; (2) dividing the image into a training set and a test set, and performing dataenhancement on the training set; (3) inputting a defective image, a non-defective image and label information of the defective image into the network at the same time every time to carry out model training; and (4) inputting the test image into the trained aluminum material surface defect detection model, and obtaining the position and the corresponding category of the defect. According to the method, a defective image and a non-defective image can be effectively utilized; the generalization ability and the detection precision of the model are improved, the detection performance is further improved by fully utilizing context information around the candidate region, the detection performance of dense small defects can be improved by utilizing a soft non-maximum suppression algorithm, and the method is an efficient aluminum material surface defect detection algorithm.

Description

technical field [0001] The invention relates to the field of image target detection, namely a deep learning-based aluminum surface defect detection algorithm. Background technique [0002] In the actual production process of aluminum materials, due to the influence of various factors, the surface of aluminum materials will have defects such as dirty spots, non-conductivity and bottom leakage, which will seriously affect the quality of aluminum materials. In order to ensure product quality, manual visual inspection is required. However, the surface of aluminum itself contains lines, which are not highly distinguishable from defects. Traditional manual inspection with naked eyes is very laborious and cannot accurately determine surface defects in a timely manner, making it difficult to control the efficiency of quality inspection. [0003] With the development of deep learning, especially the application of convolutional neural network in image recognition, image detection an...

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
Inventor 陈楚城戴宪华
Owner SUN YAT SEN UNIV
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