Nickel Foam Surface Defect Identification Based on Improved Probability Extreme Learning Machine

Author(s): Binfang Cao, Jianqi Li*, Fangyan Nie

Journal Name: Recent Advances in Computer Science and Communications
Formerly Recent Patents on Computer Science

Volume 13 , Issue 4 , 2020

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Graphical Abstract:


Background: In the nickel foam production process, the detection and identification of surface defects relies heavily upon the operators’ experiences. However, the manual observation is of high labor intensity, low efficiency, strong subjectivity and high error rate.

Objective: Therefore, this paper proposes a new method for the nickel foam surface defect detection and identification, based on an improved probability extreme learning machine.

Methods: At first, a machine vision system for nickel foam is established, and gray level cooccurrence matrix is used to calculate defect features, which are inputted into extreme learning machine to train the defect classifier. Then a composite differential evolution algorithm is used to optimize the input weights and hidden layer thresholds. Finally, an integrated probabilistic ELM is proposed to avoid misjudgments when multiple probabilities values are almost identical.

Conclusion: Experiments show that the proposed method can achieve a defect-identifying accuracy, which meets an enterprise’s needs.

Keywords: Nickel foam, defect recognition, ELM, characteristic parameter, differential evolution algorithm, gray level occurrence matrix.

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Article Details

Year: 2020
Published on: 18 October, 2020
Page: [604 - 610]
Pages: 7
DOI: 10.2174/2213275912666190219131856
Price: $25

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