Multitarget fiber high-precision position detection method based on a front light source

Zhou, Zengxiang; Duan, Shipeng; Zuo, Jiale; Li, Mengtao; Liu, Zhigang; Hu, Hongzhuan; Wang, Jianping; Chu, Jiaru

doi:10.1117/1.jatis.7.1.014007

Cited by 8 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, compared with a template matching method, a deep learning method can better solve this problem. As shown in Figure 1, the ceramic ferrules were extracted from the whole image by Faster R-CNN (regionbased convolutional neural networks) (Ren et al 2015;Zhou et al 2021b) under front illumination. The final result returned by the deep learning network is the corner coordinates and dimensions of each rectangular box including the complete ceramic ferrules.…”

Section: Preidentification Of Ceramic Ferrulesmentioning

confidence: 99%

Research on Autofocus Recognition of the LAMOST Fiber View Camera System Under Front and Back Illumination

Liang¹,

Zhou²,

Duan³

et al. 2022

PASP

Self Cite

View full text Add to dashboard Cite

In the closed-loop detection system of the LAMOST, the lens of the fiber view camera system must focus the end of the optical fiber to accurately acquire the fiber position. It is difficult to evaluate the fibers image with a very small proportion of the image with the traditional autofocus algorithm, whether it is recognition of the front illuminated by the fiber ceramic ferrules, or the light-emitting spot of the fibers in the black background, that is, the recognition of the back illumination of the fibers. In this paper, we propose an autofocus determination method for the LAMOST closed-loop control under front and back illumination conditions. Under the condition of front illumination, to greatly reduce the calculation time, the system first pre-recognizes the focusing target through the Faster R-CNN and then uses an optimized contrast algorithm to evaluate the image definition of the corrected focusing target ROI. Then, the algorithm is compared with Tenengrad gradient and Laplacian gradient calculations using the Sobel operator and Laplacian operator in OpenCV. The results show that this method takes only one-ninth of the time required by the other methods to obtain the same accuracy. Under the condition of back illumination, we use the average number and average brightness of spot pixels as the evaluation basis of image sharpness. This method can complete the evaluation of image sharpness in the process of spot recognition and provide initial data for the subsequent detection for closed-loop control. The focus accuracy of the camera is of great significance for thousand-fiber metrology, which will have an important influence on the accuracy of astronomical observations. These focusing methods not only play an important role in the closed-loop control of the LAMOST but also apply to the focusing of closed-loop detection systems of other multi-target optical fiber spectral astronomical telescopes.

show abstract

Section: Preidentification Of Ceramic Ferrulesmentioning

confidence: 99%

Research on Autofocus Recognition of the LAMOST Fiber View Camera System Under Front and Back Illumination

Liang¹,

Zhou²,

Duan³

et al. 2022

PASP

Self Cite

View full text Add to dashboard Cite

show abstract

“…It detects the center of the ferrule rather than directly detecting the fiber position, eliminating complicated lighting in the spectrometer and saving switching time. However, currently, the measurement accuracy with front-illumination is lower than that with back-illumination (Zhou et al 2021b).…”

Section: Introductionmentioning

confidence: 98%

The Calibration of theta-phi Fiber Positioners Based on the Differential Evolution Algorithm

Zhang,

Huang,

Chen

et al. 2024

Self Cite

View full text Add to dashboard Cite

Robotic fiber positioner (RFP) arrays are commonly adopted in multiobject spectroscopic instruments. The positioning accuracy is a common but vital issue for RFP as inaccurate fiber placement may heavily affect the observation performance. The calibration of RFP can effectively improve the positioning accuracy. Least-square is a widely used calibration method. However, it has disadvantages, such as sensitivity to the initial values and calculation complexity. To improve the positioning accuracy and reduce the iteration moves, we propose a new calibration method based on the differential evolution algorithm and verify it by calibrating the RFP of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope. We first build the kinematic models of the RFP based on the Denavit–Hartenberg matrix and geometry relationship. Then, we analyze the error components and present the proposed calibration algorithms. The experiments are done with the digital universal tool microscope 19JC and the errors are calculated using the distance between the positions of achieved and target. Results show that the proposed algorithm can achieve higher accuracy than the least-square method and the average positioning accuracy is improved by 78.94% after calibration. Combined with the “pulse reduction” strategy and close-loop compensation, after two moves, the positioners can place the fiber ends within 40 μm of the intended location. The proposed calibration method is also suitable for other similar theta-phi positioners.

show abstract

“…In this way, light enters fibers from the spectrometer, and the other end of the fibers is imaged in a vision system. However, the illumination device brings mechanical instability and light pollution in the spectrograph and reduces the efficiency of astronomical observations [14] . Following, the front-illuminated method has been presented instead.…”

Section: Introductionmentioning

confidence: 99%

A real-time aimer based on special-shaped microlens and optical fiber bundle

Wang,

Gan

et al. 2023

Optoelectronic Devices and Integration XII

View full text Add to dashboard Cite

Multitarget fiber high-precision position detection method based on a front light source

Cited by 8 publications

References 0 publications

Research on Autofocus Recognition of the LAMOST Fiber View Camera System Under Front and Back Illumination

Research on Autofocus Recognition of the LAMOST Fiber View Camera System Under Front and Back Illumination

The Calibration of theta-phi Fiber Positioners Based on the Differential Evolution Algorithm

A real-time aimer based on special-shaped microlens and optical fiber bundle

Contact Info

Product

Resources

About