Feature extraction algorithm based on adaptive wavelet packet for surface defect classification
Abstract
This paper proposes a feature extraction method to effectively handle the textural characteristics in images with defects in cold rolled strips. An adaptive wavelet packet scheme is developed to produce the optimum number of features automatically through subband coding gain. Also four classical entropy features in the images with defects are used as local features in the spatial domain. A neural network is used to classify the defects from these features. Experiments with real image data show good training and generalization performances of the proposed method.
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Lee et al. [72] used wavelet-based methods to classify 8 types of defects in cold strip.
“Lee et al. [72] (cold strip application) and [70] (hot strip application) used wavelet-based methods to classify 8 types and 24 types of defects, respectively.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [72]
Lee et al. [72] used an adaptive wavelet (AWP) packet scheme to produce the optimum number of features through subband coding gain.
“Lee et al. [72] used an adaptive wavelet (AWP) packet scheme to produce the optimum number of features through the subband coding gain.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [72]
Three-layer NN-BP of ten hidden nodes and eight output nodes was used with standard back propagation algorithm for classification with very high accuracy of 99%.
“Three-layer NN-BP of ten hidden nodes and eight output nodes (eight types of defects) was use with standard back propagation algorithm for classification with very high accuracy of 99%.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [72]
Steel surfaces exhibit many types of defects, and NN-BP has been extensively used to classify such defects.
“Steel surfaces exhibit many types of defects, and NNBP has been extensively used to classify such defects [31,36,54,59,63,72,78,80].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [72]
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Review of vision-based steel surface inspection systems
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- Authors
- Lee, C. S., Choi, C.-H., Choi, J. Y., Kim, Y. K., & Choi, S. H.
- Affiliations
- Seoul National University (KR)
- ORCID
- Venue
- Proceedings of 3rd IEEE International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2002-12-23
- Volume
- 1
- Pages
- 673-676
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2165321742
- Cited by
- 30
- References
- 8
- Citations by year
- 2023: 1 · 2022: 2 · 2020: 1 · 2018: 1 · 2017: 1 · 2014: 1 · 2012: 1
- FWCI
- 4.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- surface defect classification, cold-rolled steel strip, feature extraction, entropy features, texture features, neural network classification
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