研究目的
To study the detection and identification technology of surface defects of optical fiber communication cables using machine vision technology.
研究成果
The algorithm studied in this paper is applied to the optical cable production line. The minimum detection area is 0.05 mm2, the diameter detection accuracy is 0.1 mm, and the defect can be real-time alarmed, which reduces the false alarm rate of dust and waterlogging. The correct detection rate of needle eyes can reach 70% under the condition of dust interference, which reduces the false alarm rate and improves the detection performance of the system.
研究不足
The method can only detect cables with a diameter of 6mm. The detection speed is only 1m/s, and the defects cannot be classified.
1：Experimental Design and Method Selection:
Utilizes mathematical geometry and linear interpolation method to convert cable cylinder into plane. Based on the gray-scale characteristics of the pinhole in the picture, gray threshold and gradient amplitude are used to improve the detection rate of the pinhole.
2：Sample Selection and Data Sources:
Optical cable images captured by three cameras are used, with each image spliced with a 30° viewing angle image redundancy.
3：List of Experimental Equipment and Materials:
Uses Visual Studio2015, QT5.7.1, OpenCV2.4.9, Windows7 system for software implementation.
4：1, OpenCV9, Windows7 system for software implementation.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Includes image preprocessing, cable area extraction, surface defect and bump determination, surface defect mark, and image stitching.
5：Data Analysis Methods:
Gradient amplitude and OTSU algorithm are used to determine the conventional defects and pinholes of the cable area.
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