Workpiece classification based on transfer component analysis

• Published in: Wireless networks 2024-08, Vol.30 (6), p.4935-4947
• Main Authors: Qiao, Liyong, Zhang, Shuang, Liu, Chungang, Jin, Huilong, Zhao, Hua, Yao, Jian, Cao, Lingru, Ji, Yujia
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Communications Engineering; Computer Communication Networks; Data augmentation; Electrical Engineering; Engineering; Error analysis; Error correction; Error reduction; IT in Business; Machine vision; Networks; Performance evaluation; Principal components analysis; Support vector machines; Vision systems; Workpieces
• ISSN: 1022-0038; 1572-8196
• DOI: 10.1007/s11276-022-03173-9

Classification algorithm is the crucial technique for machine vision system. The Transfer Component Analysis has been introduced for workpiece classification system. The dataset with five classes of workpieces is constructed for experiments, and the data augmentation strategies have been employed to prevent class imbalance problems. The Histogram of the Oriented Gradient features are extracted over workpiece images, and they are transformed by the Transfer Component Analysis and the Principal Component Analysis separately. The Maximum Mean Discrepancy distances have been employed to measure the distribution distances between the training set and the testing set. And the results indicate that the Transfer Component Analysis has dramatically reduced the distribution differences in a dimensionality reduction subspace, better than the Principal Component Analysis. The Error Correction Output Code is combined with Support Vector Machine and K-Nearest Neighbor respectively as the classifiers. The macro-averaged F 1 measure is adopted to evaluate the classifiers performance. Experiment results show that, the Transfer Component Analysis is particularly beneficial for improving the classification performance, and the Support Vector Machine is more favorable than the K-Nearest Neighbor for workpiece classification. The method of the Transfer Component Analysis combined with the Support Vector Machine possesses the best classification performance among these methods in this paper.