Intelligent waste lens detection method based on multi-index driving

Through the multi-index intelligent detection method of waste lenses, combined with deep learning optical flow calculation and video quality evaluation, the problem of insufficient intelligence and adaptability of waste lens detection in the existing technology is solved, and efficient and accurate detection effects are achieved in complex scenarios.

CN119741288APending Publication Date: 2025-04-01CHENGDU RAGE&EAST ELECTRONICS CO LTD
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Patent Information

Application Number
CN202411925993.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-25
Publication Date
2025-04-01

AI Technical Summary

Technical Problem

The existing waste lens detection methods lack intelligent and adaptive quality assessment, making them difficult to adapt to multiple lighting changes, complex backgrounds and fast motion scenarios, and traditional optical flow calculation methods cannot effectively detect violent jitter in video.

Method used

Intelligent detection methods for waste lenses driven based on multi-index are adopted, including pre-processing of video frames, application of deep learning optical flow computing models, calculation of clarity and exposure, as well as segmented quality evaluation and defect merging.

Benefits of technology

It improves the accuracy and robustness of waste lens detection, and can accurately detect waste lenses in videos in complex scenarios, achieving more efficient and intelligent video quality evaluation.

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Abstract

The invention discloses a waste shot intelligent detection method based on multi-index driving, and the method comprises the steps: inputting a video, collecting a video frame of an input video stream, and carrying out the preprocessing of the video frame; in the same scene, carrying out definition and exposure calculation on the processed video frames; in the same scene, an optical flow vector field between two video frames is calculated through a deep learning optical flow calculation model, and an optical flow range is evaluated; and performing quality evaluation on the video frames in a segmented manner, and merging and outputting defective segment waste shots. According to the scheme, the deep learning model and a traditional image quality evaluation method are combined, strenuous motion and jitter in the video are judged through deep learning calculation optical flow and local optical flow intensity evaluation, and the accuracy of waste lens detection is improved.
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