A FPC defect detection method based on improved mask RCNN

Abstract

The invention discloses an FPC defect detection method based on improved MASK RCNN, the steps are as follows: S1. ROI processing and image cutting, collecting target detection images, preprocessing the FPC original image data, and cutting into small images suitable for network input; S2 . Perform data enhancement processing on the preprocessed image to expand the data set for model training; S3. Manual labeling; S4. Determine the number N of horizontal stacking of feature map blocks, and determine the horizontal stacking of feature map blocks for the type and quantity of FPC defect detection Number of times N; S5. Model training, send the marked image data set into the improved MAKS RCNN network model for training; S6. Perform performance evaluation on the trained FPC defect detection model; S7. Parameter optimization and fine-tuning, combined with the evaluation results of S6 , to further optimize the model. The invention realizes the automatic detection of FPC defects, and the size of the defects can be obtained by segmenting the image defects through the mask, which is convenient for staff to re-judgment, and greatly reduces the detection cost of the enterprise.

Description

technical field [0001] The invention relates to the technical field of defect detection and image segmentation of flexible circuit boards, in particular to an FPC defect detection method based on improved MASKRCNN. Background technique [0002] Flexible printed circuit board is referred to as FPC (Flexible Printed Circuit Board), which has the characteristics of thin thickness, light weight, and free bending and folding. Compared with traditional circuit boards, FPC takes up less space and can greatly reduce package size and weight to meet the requirements of high integration and mobility of electronic products. It can realize three-dimensional space wiring of circuits and enhance product reliability. Reduce assembly costs. Because FPC materials are special, highly integrated, and the process is complex, the manufacturing process is easily affected by factors such as equipment, personnel, and the environment, resulting in defects. [0003] The MASK RCNN network is an impro...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a FPC defect detection method based on improved MASK RCNN, which realizes automatic detection, classification and segmentation of FPC main defects, saves labor costs of enterprises, and greatly improves Efficiency of defect detection

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