Mobile phone casing defect detecting method based on depth learning

Abstract

The invention relates to a mobile phone casing defect detecting method based on depth learning. The method comprises the steps that (1) the image of a mobile phone casing to be detected is acquired and pre-processed; and (2) the pre-processed image is input into a pre-trained defect detection model for defect detecting to acquire the position of a defect on the mobile phone casing, and the confidence of the position as the defect is provided. The defect detection model is a depth network based on depth learning, and comprises a feature extraction network and a classifier and regression device network, wherein the feature extraction network and the classifier and regression device network are in successive cascade. The feature extraction network carries out feature extraction on the pre-processed image to acquire a feature image. The classifier and regression device network classifies and regresses the feature image to acquire the defect position and the confidence of the mobile phone casing. Compared with the prior art, the method provided by the invention has the advantages of high detection precision and accurate and reliable detection result.

Description

technical field [0001] The invention relates to a method for detecting defects in a mobile phone casing, in particular to a method for detecting defects in a mobile phone casing based on deep learning. Background technique [0002] With the popularity of mobile phones and their rapid replacement, there is a huge demand for the output of mobile phone shell products in industrial production lines. During the entire process from ingredients to final molding, due to transportation, production process, accidents, etc., there are often various defects (such as dents, scratches, abrasions, uneven color, etc.) on the mobile phone shell, and these defects Products that affect their performance or degrade the user experience are not allowed to enter the market. Although in the past ten years, the production of industrial products has made great progress and the demand for production has been increasing, the defect detection of related industrial products still relies on manual comple...

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Problems solved by technology

[0006] Similar defect detection methods that use traditional image processing and feature extraction as the main means have also been applied to solar panel defect detection, steel rail defect detection, LED defect detection and other fields, but for the defect detection of mobile phone shells, due to The defect of the mobile phone shell has the characteristics of small area, extremely slight, various defect forms, and low contrast with the background. The above-mentioned traditional algorithm cannot be well applied to the defect detection of the mobile phone shell. However, none of them can meet the needs of industrial production

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