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Intelligent leather defect classification using Fourier angular radial partitioning algorithm with ensemble classifier
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AI
DOI：10.1007/s11042-023-16224-w.png)
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Abstract
En 中文
Leather quality inspection is essential in determining the usable area of the material. As leather is a natural substance, surface defects can have a significant impact on its overall quality and reduce its usability. The automatic identification of surface defects in leather holds great importance in the inspection process. This study presents an innovative method called the Fourier Angular Radial Partitioning (FARP) algorithm for extracting features, specifically tailored for the identification of surface defects in leather. A cutting-edge industrial prototype machine vision system is designed with innovative capabilities to acquire high-quality entire leather surface image accurately. The FARP algorithm leverages a combination of spatial and radial distributed invariant feature descriptors obtained from the magnitude of the Fourier Transform. Furthermore, by partitioning the image into multiple sub-regions enables the FARP to extract features to effectively analyze both prominent flaws like cuts, scars and subtle imperfections like pinholes. The performance of the proposed FARP algorithm is compared to Gray Level Co-occurrence method and Spatial domain features. Correlation analysis is conducted on the extracted features from these three methods to identify the optimal feature set. Leather defects are classified using a multinomial logistic regression model and an ensemble classifier approach with random forest. Various measures, including accuracy, specificity, sensitivity, F-score, Mathew Correlation Coefficient, and ROC analysis using Z-test, are employed for a comprehensive evaluation. The experimental results indicate that the random forest and the proposed FARP feature set, achieves a remarkable classification accuracy of 88.67% and a notable area under the ROC curve of 0.875. This intelligent solution, which integrates FARP with the Random Forest classifier, surpasses the performance of manual expert leather defect classification, highlighting its superior effectiveness.
Keywords:
Leather defect
Classification
Machine learning
Ensemble classifier
Fourier angular radial partitioning
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3
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2.0W
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3.2W
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