Abstract
This paper presents a novel method for defect detection in pipes using a mobile laser-optics technology and conventional digital-geometry-based image processing techniques. The laser-optics consists of a laser that projects a line onto the pipe's surface, and an omnidirectional camera. It can be mounted on a pipe crawling robot for conducting continuous inspection. The projected laser line will be seen as a half-oval in the image. When the laser line passes over defected points, the image moments on the pixel information would change. We propose a B-spline curve fitting on the digitally-convoluted image and a curvature estimation algorithm to detect the defects from the image. Defect sizes of 2 mm or larger can be detected using this method in pipes of up to 24 inch in diameter. The proposed sensor can detect 180-degree (i.e., upper half surface of the pipe). By turning the sensor 180 degrees, one will be able to detect the other half (i.e., lower half of the pipe's surface). While, 360-degree laser rings are available commercially, but they did not provide the intensity needed for our experimentation. We also propose a fast boundary extraction algorithm for real time detection of defects, where a trace of consecutive images are used to track the image features. Tests were carried out on PVC and steel pipes.
Highlights
Automated surface inspection of pipes refers to a class of methods and algorithms which detect, classify, localize and measure surface defects on the interior surface of pipes
This paper presents a novel method for defect detection in pipes using a mobile laser-optics technology and conventional digital-geometry-based image processing techniques
In this paper we focus on visual inspection of the interior surface of pipes using a single-view omnidirectional imaging sensor and a laser line used as the structured light image fringe
Summary
Automated surface inspection of pipes refers to a class of methods and algorithms which detect, classify, localize and measure surface defects on the interior surface of pipes. Al used a perspective camera and circleprojecting LED ring for visual inspection of small sewer pipes, [1,2]. They used a pinhole camera to image the LED ring, and used artificial Neural Networks for analyzing the brightness of the LED ring to detect the defects. Given the limited Field of View (FOV) of the pinhole cameras, the LED ring must be projected at far to be visible.
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