A defect detection method and system for a high-resolution image

Abstract

The invention discloses a defect detection method and system for a large-resolution image, and the method comprises the following steps of S1, carrying out the transverse and longitudinal sliding on adefect image through employing a sliding window with a certain size, and obtaining a plurality of partition images; S2, carrying out the defect detection on each partition image through a target detection model obtained based on small-resolution image training, obtaining a result set describing a target detection box, wherein the detection result of each target detection box comprises a probability classification value of a defect type contained in the target detection box; S3, calculating the Euclidean distance between two adjacent target detection frames in the result set in sequence, whenthe Euclidean distance is smaller than a window fusion threshold value, fusing the two target detection frames, and taking the target detection frame with the maximum probability classification valueas a defect detection result. According to the present invention, the defect detection of a large-resolution image is realized through a sliding segmentation and window fusion mode, the micro defectscannot be lost, and the overdetection or missing detection is avoided.

Description

technical field [0001] The invention belongs to the technical field of automatic defect detection, and more specifically relates to a large-resolution image defect detection method and system based on deep learning. Background technique [0002] In the process of LCD panel production, defects such as scratches, dust, and stains on the backlight unit (BLU) often lead to similar defects on the final LCD panel, which directly affects the final quality and quality of the LCD panel. Product-level output results, therefore, it is critical to detect possible defects on the BLU backlight panel in the early stages of the LCD manufacturing process. [0003] At present, the defect detection on the BLU backlight board is mainly realized through the following three methods. One is to manually detect one by one by the quality inspector; the second is to detect with the help of traditional image processing algorithms; Detection, deep learning Since its emergence in the ImageNet image reco...

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Problems solved by technology

[0007] Aiming at at least one defect or improvement requirement of the prior art, the present invention provides a defect detection method and system for a large-resolution image, the purpose of which is to solve the problem that the existing detection method cannot directly detect a large-resolution image and is prone to over-inspection. The problem of missed detection

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