LED light splitting ribbon real-time defect detection compensation method and system based on edge computing

By using a lightweight neural network model and real-time image processing on an edge computing device, the problems of real-time performance, accuracy, and network dependence in LED beam splitting and tape-making inspection were solved. High-precision defect detection and position compensation under high-speed motion were achieved, improving detection accuracy and production stability, and supporting continuous iteration of models and processes.

CN122415522APending Publication Date: 2026-07-17SHENZHEN SITUOAN OPTOELECTRONICS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN SITUOAN OPTOELECTRONICS CO LTD
Filing Date
2026-04-22
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies for LED beam splitting and tape-making inspection suffer from a contradiction between real-time performance and accuracy, strong dependence on networks, difficulty in model updates, and low data utilization, making it difficult to achieve high-precision defect detection and micron-level position compensation under high-speed motion.

Method used

A real-time defect detection method based on edge computing is adopted. A lightweight deep neural network model is used to perform image processing on an edge computing device. Combined with motion blur restoration and adaptive threshold segmentation, real-time rejection and compensation instructions are generated and uploaded to the cloud system asynchronously to build a closed loop of edge AI inference and control.

Benefits of technology

It achieves a detection-compensation response within 50ms, with a position compensation accuracy of ±10 micrometers and a detection accuracy of 99.5%, reducing network dependency risks and enabling data-driven continuous optimization.

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Abstract

The application discloses an LED light splitting ribbon real-time defect detection compensation method and system based on edge calculation. The method comprises the following steps: obtaining an LED target image collected for pre-processing to obtain an image sequence; using a network model deployed on an edge calculation device to perform real-time inference on the image sequence to obtain an inference result, wherein the inference result comprises an appearance defect and a position offset; generating a rejection control instruction according to the appearance defect, and performing online rejection on the LED chip with defects before entering the ribbon process; generating a real-time motion control compensation instruction according to the position offset, and sending the real-time motion control compensation instruction to a pick-and-place execution mechanism of a ribbon machine to correct the placement position of the LED chip; and uploading the detection result, compensation data and device state information processed by the edge calculation device to a cloud manufacturing execution system asynchronously. The application improves the mounting precision and yield, realizes edge-cloud collaborative real-time defect detection, fuses visual and force sensing double compensation, and improves the detection precision.
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