Automated surface feature detection using fringe projection: An autoregressive modeling-based approach. (October 2019)

• Record Type: Journal Article
• Authors: Kulkarni, Rishikesh; Banoth, Earu; Pal, Parama
• Is Part Of: Optics and lasers in engineering. Volume 121(2019)
• Page Start: 506
• Page End: 511
• Publication Date: 2019-10
• Subjects: Fringe projection profilometry -- Surface under test -- Defect identification -- Auto-regressive model -- Non-destructive testing; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2019.05.014
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: Images acquired using fringe projection are modelled as an autoregressive process. Fringe frequencies are estimated using AR coefficients using Yule–Walker equations. Deviations from mean frequency indicates the presence of surface features/defects. Our algorithm provides robust, computationally light defect localization. Abstract: An automatic surface defect detection algorithm is proposed in a fringe projection profilometry setup. An exponential phase field associated with the fringe projected surface image is analyzed using two-dimensional auto-regressive (AR) model. The AR model coefficients capture the local fringe frequency information. The variation in the fringe frequency from the defect-free to defective surface region is utilized as a signature for defect identification and localization. The fringe frequency threshold required for the image segmentation into defective and defect free regions is derived from the mean frequency computed over the same image which obviates the need for the reference image. In order to enhance the computation efficiency of the algorithm the image is divided into small patches and analysis is performed patch-wise. The simulation and experimental results demonstrate the advantages of the proposed phase based surface evaluation method over the commonly used intensity based methods.