Abnormality detection strategies for surface inspection using robot mounted laser scanners
journal contributionposted on 10.04.2018, 08:25 by Sara Sharifzadeh, Istvan Biro, Niels LohseNiels Lohse, Peter KinnellPeter Kinnell
The detection of small surface abnormalities on large complex free-form surfaces represents a significant challenge. Often surfaces abnormalities are less than a millimeter square in area but, must be located on surfaces of multiple meters square. To achieve consistent, cost effective and fast inspection, robotic or automated inspection systems are highly desirable. The challenge with automated inspection systems is to create a robust and accurate system that is not adversely affected by environmental variation. Robot-mounted laser line scanner systems can be used to acquire surface measurements, in the form of a point cloud1 (PC), from large complex geometries. This paper addresses the challenge of how surface abnormalities can be detected based on PC data by considering two different analysis strategies. First, an unsupervised thresholding strategy is considered, and through an experimental study the factors that affect abnormality detection performance are considered. Second, a robust supervised abnormality detection strategy is proposed. The performance of the proposed robust detection algorithm is evaluated experimentally using a realistic test scenario including a complex surface geometry, inconsistent PC quality and variable PC noise. Test results of the unsupervised analysis strategy shows that besides the abnormality size, the laser projection angle and laser lines spacing play an important role on the performance of the unsupervised detection strategy. In addition, a compromise should be made between the threshold value and the sensitivity and specificity of the results.
The authors acknowledge support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation, and the funding support from EPSRC for this work as part of grant EP/L01498X/1.
- Mechanical, Electrical and Manufacturing Engineering