YOLO-FOA: A lightweight rotational target detection algorithm based on improved YOLO for optical fiber robot

Abstract

In fiber optic communication, as networks expand, precise detection and alignment of fiber optic adapters are crucial for enhancing system stability and transmission quality. Traditional target detection algorithms face two main issues in fiber optic adapter detection: inability to handle arbitrarily oriented targets and difficulty in efficient deployment on embedded devices. To tackle these issues, this paper introduces a lightweight rotating target detection algorithm, YOLO-FOA, for fiber optic communication scenarios. The algorithm is based on the YOLO model, which significantly reduces the computational and parametric quantities of the model by introducing Dynamic Head and Dynamic ATSS, and the C2f_MViTBv3, C2f_GhostBlockv2 modules and Angle DFL Loss are designed to improve the detection accuracy. In addition, the dynamic alignment correction mechanism can be effectively applied to intelligent calibration and real-time deviation correction in fiber optic communication networks. Experiments show YOLO-FOA achieves 97.1% detection accuracy on a self-constructed dataset, outperforming the baseline model by 1.3%, with a 4.5% reduction in parameters and 7.2% in computation. Suitable for embedded devices due to its high accuracy and low resource demands, YOLO-FOA offers a new approach to enhancing fiber optic communication system stability and transmission quality.

Keywords

Real-time target detection / Lightweight network / Dynamic alignment correction / Fiber optic adapter / Directional bracketing frame

CRediT authorship contribution statement

Yingqi Wu: Writing – review & editing, Writing – original draft. Jialong Chen: Writing – review & editing, Data curation. Xiuli Yu: Funding acquisition. Jian Li: Funding acquisition, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the interdisciplinary Team of Intelligent Elderly Care and Rehabilitation in the “Double first-class” Construction (530324004), and Talent Introduction Project (510224074) of Beijing University of Posts and Telecommunications, China .

Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.birob.2026.100273.

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