Neighborhood preserving perceptual fidelity aware MSE for visual inspection of industrial flat surface products

Article type: Research Article

Authors: Ameen, Mustafa^{; *} | Alrahmawy, Mohammed | AbouEleneen, Amal | Tolba, Ahmad

Affiliations: Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt

Correspondence: [*] Corresponding author. Mustafa Ameen, Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt. E-mail: mustafameen2001@gmail.com.

Abstract: Automated visual inspection is becoming an important field of computer vision in many industries. The real-time inspection of flat surface products is a task full of challenges in industrial aspects that requires fast and accurate algorithms for detection and localisation of defects. Structural, statistical and filter-based approaches, such as Gabor Filter Banks, Log-Gabor filter and Wavelets, have high computational complexity. This paper introduces a fast and accurate model for inspection and localization of industrial flat surface products: Neighborhood Preserving Perceptual Fidelity Aware Mean Squared Error (NP-PAMSE). The Extreme Learning Machine (ELM) is used for classification. ELM is found to be the perfect classifier for detecting defects. The proposed model resulted in defect detection accuracy of 99.86%, with 98.16% sensitivity, and 99.90% specificity. These results show that the proposed model outperforms many existing defect detection approaches. The discriminant power displays the efficiency of ELM in differentiation between normal and abnormal surfaces.

Keywords: Automated visual inspection (AVI), perceptual fidelity aware mean squared error (PAMSE), extreme learning machine (ELM)

DOI: 10.3233/JIFS-192071

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1183-1196, 2020