Online small-object anti-fringe sorting of tobacco stem impurities based on hyperspectral superpixels.
Spectrochim Acta A Mol Biomol Spectrosc
; 302: 123084, 2023 Dec 05.
Article
de En
| MEDLINE
| ID: mdl-37423100
ABSTRACT
The use of tobacco stems as raw material for cigarettes reduces cost and improves the flammability of cigarettes. However, various impurities, such as plastic, reduce the purity of tobacco stems, degrade the quality of cigarettes, and endanger the health of smokers. Therefore, the correct classification of tobacco stems and impurities is crucial. This study proposes a method based on hyperspectral image superpixels and the use of light gradient boosting machine (LightGBM) classifier to categorize tobacco stems and impurities. First, the hyperspectral image is segmented using superpixels. Second, the gray-level co-occurrence matrix extracts the texture features of superpixels. Subsequently, an improved LightGBM is applied and trained with the spectral and textural features of superpixels as a classification model. Several experiments were implemented to evaluate the performance of the proposed method. The results show that the classification performance based on superpixels is better than that based on single-pixel points. The classification model based on superpixels (10 × 10 px) achieved the highest impurity recognition rate (93.8%). This algorithm has already been applied to industrial production in cigarette factories. It exhibits considerable potential in overcoming the influence of interference fringes to promote the intelligent industrial application of hyperspectral imaging.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Type d'étude:
Prognostic_studies
Langue:
En
Journal:
Spectrochim Acta A Mol Biomol Spectrosc
Sujet du journal:
BIOLOGIA MOLECULAR
Année:
2023
Type de document:
Article
Pays d'affiliation:
Chine