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Center-based Transfer Feature Learning With Classifier Adaptation for surface defect recognition
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DOI：10.1016/j.ymssp.2022.110001.png)
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摘要
En 中文
Surface defect recognition using Deep Learning based computer vision techniques is an important task in industrial manufacturing. However, surface images have different distributions due to different environments in industrial manufacturing, where the distribution difference between images will degrade the accuracy of Deep Learning based computer vision techniques. In addi-tion, existing Transfer Feature Learning (TFL) methods reduced the distribution difference only by the location parameters of distributions, which ignored the effect of scale parameters in representing the distribution. To overcome these problems, we propose Center-based Transfer Feature Learning with Classifier Adaptation (CTFLCA) for surface defect recognition. First, to eliminate the distribution difference at the feature layer from the location parameters and scale parameters of distributions, we utilize centers as bases to propose the Center-based Transfer Feature Learning method (CTFL) by minimizing Center-based Distribution Difference of Location Parameters (CDDLP) and Center-based Distribution Difference of Scale Parameters (CDDSP). Second, to reduce the distribution difference at the classifier layer from the location parameters and scale parameters, we establish the Center-based Classifier Adaptation method (CCA) using a similar idea of CTFL, where the optimization objective of CCA is formulated by minimizing the classification errors, CDDLP in classification results, and CDDSP in classification results. Next, under the guidance of the class-wise sample selection, we establish CTFLCA for integrating CTFL with CCA. Finally, sufficient results on four datasets (NEU-C, PR-C, Office-Caltech, and ImageCLEF-DA) illustrate the effectiveness of CTFLCA, where the average classification accu-racies of CTFLCA are 99.6%, 98.0%, 95.1%, and 91.1%.
Keyword:
Classifier adaptation
Distribution difference
Surface defect recognition
Transfer Feature Learning
期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
机构
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