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在微纳光纤拉锥制备过程中，对直径的大范围、高精度和动态实时性测量是实现低损耗传输和色散调控的关键.针对现有传统制备方法直径调控范围小、操作复杂及耗时长等问题，本文首次基于深度学习神经网络算法实现了微纳光纤自动检测系统.本文利用计算机视觉中的图像分割方法，通过制作高质量多尺度微纳光纤数据集，使用基于小目标检测改进的YOLOv8−FD (You Only Look Once version 8-Fiber Detection)算法对微纳光纤直径进行自动检测.在数据集中获得了平均均值精度高达mAPIoU=50=0.995和mAPIoU=50-95=0.765的性能参数.实验结果表明，该系统可实现微纳光纤直径462 nm−125 μm范围，误差2.95%以内的测量和自动化制备，并随着光纤直径增长，误差逐渐缩小，且该系统光学成像单个像素分辨率为65.97 nm，平均检测时间为9.6 ms.本文工作适用于对微纳光纤的高精度实时测量和自动精确制备，为低损耗传输和色散可调的微纳光纤器件发展提供新的思路.The wide range, high precision, and dynamic real-time measurement of diameter are crucial for achieving low loss transmission and controlling dispersion in the preparation process of micro-/nanofiber. In view of the problems of small diameter regulation range, complex operation and long-time consumption of the existing preparation methods, this paper firstly realizes the automatic detection system of micro-/nanofiber based on deep learning neural network algorithm. In this paper, the image segmentation method in computer vision is used to make high-quality multi-scale micro-/nanofiber datasets, and the improved YOLOv8-FD(You Only Look Once version 8-Fiber Detection) algorithm based on small target detection is used to automatically detect the diameter of micro-/nanofiber.
Through image segmentation and identification of the target of single pixel size in the microscopic image, the diameter detection of micro-/nanofiber is finally realized. In this process, the real-time diameter of micro-/nanofiber is obtained through image information, and then the micro-/nanofiber small target is accurately segmented to achieve the precise detection of mAPIoU=50=0.995 and mAPIoU=50:95=0.765 on the micro-/nanofiber multi-scale target dataset with extremely high accuracy. The algorithm-based construction of a high-precision micro-/nanofiber automatic preparation system enables real-time accurate segmentation of fiber edges, calculation of fiber diameter, and feedback to the control system for achieving automated preparation of fibers with arbitrary diameters. Additionally, it facilitates detection of micro-/nanofiber within the range of 462 nm to 125 μm. The average response time for reasoning is 9.6 ms, while maintaining a detection error below 2.95%.
In addition, compared with other micro-/nanofiber diameter detection methods based on optical imaging and mode cutoff, this method shows advantages of high precision, high speed and arbitrary diameter preparation for diameter detection based on deep learning neural networks. The system is well-suited for high-precision real-time measurement and automated, precise preparation of micro-/nanofiber, thereby offering a novel approach for the development of low-loss transmission and adjustable dispersion micro-/nanofiber devices.
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Keywords:
- micro-/nanofiber /
- diameter measurement /
- deep learning /
- image segmentation
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