研究目的
To design a non-contact, real-time reliable and high-automatic fiber-optic intelligent defect detection method to improve the detection accuracy and the intelligence level of the enterprise.
研究成果
The DSSD-based optical fiber defect detection method proposed in this paper achieves a high detection rate of 96.7%, demonstrating significant improvements in detection time, efficiency, and accuracy over traditional methods. Future research should expand the database to include more defect types and explore faster algorithms for industrial applications.
研究不足
The study focuses on three specific types of optical fiber defects and may not cover all possible defects. The algorithm's performance in real-world production environments needs further validation.
1：Experimental Design and Method Selection:
The study uses a DSSD algorithm for detecting optical fiber surface defects, incorporating ResNet-101 to enhance feature extraction.
2：Sample Selection and Data Sources:
A database of three kinds of optical fiber defect samples was established and augmented.
3：List of Experimental Equipment and Materials:
Industrial camera model XYG300 with a pixel of
4：0 Megapixels was used for image acquisition. Experimental Procedures and Operational Workflow:
Images were pre-processed, data was enhanced through rotation, horizontal and vertical migration, tangential transformation, and random rotation. The network was trained with a varying learning rate.
5：Data Analysis Methods:
The performance of the DSSD algorithm was evaluated based on detection rate and compared with other algorithms.
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