Accepted
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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