基于机器视觉识别的中药渣分选与质量分级方法
By establishing a joint model of camera and light source response and using an improved VMamba model for adaptive updates, the inconsistency problem in the sorting and quality grading of Chinese medicinal residue under light source aging and camera response drift was solved, achieving stable sorting and grading results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEBEI UNIV OF TECH
- Filing Date
- 2026-04-22
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to maintain the stability and consistency of sorting and quality grading of medicinal herb residues without interrupting production, especially under conditions of light source aging and camera response drift, leading to inconsistent sorting and grading results.
By collecting a continuous sequence of images of Chinese medicinal residue on the conveying line, recording exposure, white balance gain, and light source driving quantity, a joint response function model of camera response and light source response is established, a frame-by-frame system transfer function sequence is generated, and transfer function inversion compensation and reflectivity uniformity are performed. Combined with the improved VMamba model, asymmetric state space scanning and cross-scale state transfer are carried out. The TENT algorithm is used for label-free adaptive updates, the parameter update amplitude is limited, and an adaptive normalized parameter set is generated for sorting and grading.
It improves the consistency of imaging photometric intensity and batch-to-batch comparability, enhances the stability and robustness of identifying Chinese medicinal residues across operating conditions, improves the consistency of sorting category determination and quality grade determination, and enhances the reliability of diversion control commands and graded recording data.
Smart Images
Figure CN122090426B_ABST