Surface defect detection method, terminal device and storage medium

Abstract

The invention relates to a surface defect detection method, a terminal device and a storage medium, and the method comprises the steps: S1, collecting defect images of a detection object to form a training set, and marking the defect images in the training set; S2, constructing a defect detection model, and inputting the training set into the defect detection model for training to obtain a trained defect detection model; wherein the defect detection model is constructed based on a Faster R-CNN network, a feature extraction network of the defect detection model is a VGG-16 network, and the output of the third layer and the output of the fifth layer are superposed in the VGG-16 network; S3, inputting a defect image to be detected into the trained defect detection model to obtain a defect positioning frame and a defect type in the defect image; and S4, segmenting the defect according to the defect positioning frame in the defect image to be detected. Based on the Faster R-CNN network and the threshold segmentation method, defect types, positions and contours can be output only by inputting images in the detection process, and then end-to-end detection of defects is achieved.

Description

technical field [0001] The present invention relates to the technical field of image detection, in particular to a surface defect detection method, a terminal device and a storage medium. Background technique [0002] Not only are there many types of steel surface defects, but the distribution is not fixed. A steel surface image to be inspected may have multiple types of defects; in addition, the problem of uneven illumination during image acquisition will interfere with the machine inspection process. Therefore, traditional production enterprises are still at the stage of manual steel surface inspection. In the production process of hot-rolled strip steel, because the production temperature is as high as hundreds of degrees Celsius, manual quality inspection can only be carried out at the end of the coil. The method of random inspection after production is not only unable to achieve effective control of the cause of defects, but also makes quality objections frequently occ...

AI Technical Summary

Problems solved by technology

[0004] 1) The processing process is complex, and the defect determination process requires manual design of features, which raises the threshold for monitoring system development;

[0005] 2) The generalization ability of manually designed defect features is poor, and it is not suitable for new detection objects and defect types;

[0006] 3) Limited to defect location and classification, it is temporarily impossible to segment defect contours, which makes it impossible to achieve effective negative feedback in the manufacturing process to adjust production parameters

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