Pipe Video Restoration for Rope and Power Unit Interference
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Solution Overview
Problem
Conventional pipe defect detection methods face interference from ropes and power units in video data, leading to inefficient defect recognition and increased detection costs due to poor quality video data.
Innovation Solution
A method combining SIFT corner detection, Hough transform straight line detection, and Telea's fast marching method (FMM) image restoration algorithm to eliminate interference from ropes and power units in pipe video data, enhancing data quality and detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pipe robot equipped with a high-definition camera is used to obtain internal data of a pipe, then the resolution and clarity of video data are improved, but ropes and power units appear in the video data causing interference with defect identification
Solution Approach 1:
The patent extracts and removes the harmful elements (ropes and power units) from the video data through computer vision algorithms. The SIFT algorithm detects characteristic points of ropes, and the Hough transform identifies straight line features, allowing the system to isolate and eliminate these interfering objects while preserving the pipe defect information.
Solution Approach 2:
The patent transforms the video data from RGB color space to gray scale, changing the parameter representation to enhance edge detection capability. This parameter transformation facilitates the subsequent processing steps for identifying and removing rope and power unit interference while maintaining the structural information of pipe defects.
2Productivity
If conventional pipe detection methods are used, then the detection process is simple, but defect features are easily affected by changes in the power unit leading to inefficient detection
Solution Approach 1:
The patent performs preliminary restoration of the video data by removing ropes and power units before the actual defect detection process. This preliminary action eliminates potential sources of error that would otherwise affect subsequent defect recognition, ensuring that the detection operates on clean, interference-free data.
Solution Approach 2:
The patent introduces computer vision algorithms as an intermediary processing layer between data acquisition and defect detection. This intermediary step processes the raw video data to eliminate harmful factors, creating a bridge that connects the video acquisition system with the defect detection system while filtering out interference.
Data Source
AI summary
A method for restoring video data of a pipe based on computer vision is provided. The method includes: performing gray stretching on pipe image/video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm.