RBD-YOLOv10: A Lightweight Small-Object Detector for Laser-Tracking Cooperative Targets

Laser trackers (LTs) are essential instruments for large-scale equipment assembly and in situ measurement. However, their cooperative targets, Spherically Mounted Retroreflectors (SMRs), are often small, highly reflective, and prone to interference in complex industrial environments, making accurate detection difficult. Compared with generic small-object detection, SMR detection during LT beam reacquisition is further challenged by specular highlights, halo-like blooming, and reflective background clutter, where SMRs may appear as minute bright spots with ambiguous boundaries. In this paper, we propose RBD-YOLOv10n, a lightweight detector tailored for SMRs based on the YOLOv10 framework. To improve robustness while keeping deployment efficient, we introduce three lightweight enhancements across the backbone, neck, and head, including RepNMSC, W-BiFPN, and DEHead. Validated on a custom SMR dataset, our method achieves an [email protected] of 93.24% and an [email protected]:0.95 of 78.45%. Notably, the model is extremely lightweight, with 1.98M parameters and a 4.30 MB weight file (stored in FP16). These results show that the proposed method outperforms representative baseline detectors in balancing accuracy and efficiency, supporting practical high-precision LT vision-based SMR reacquisition under industrial conditions.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2026-03-12
DOI
https://doi.org/10.3390/app16062734
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

RBD-YOLOv10: A Lightweight Small-Object Detector for Laser-Tracking Cooperative Targets

Dabao LAO, Xin Wang, Tianqi Chen
Applied Sciences
Robotics and Sensor-Based Localization
article

RBD-YOLOv10: A Lightweight Small-Object Detector for Laser-Tracking Cooperative Targets

Dabao LAO, Xin Wang, Tianqi Chen
article en

Abstract

Laser trackers (LTs) are essential instruments for large-scale equipment assembly and in situ measurement. However, their cooperative targets, Spherically Mounted Retroreflectors (SMRs), are often small, highly reflective, and prone to interference in complex industrial environments, making accurate detection difficult. Compared with generic small-object detection, SMR detection during LT beam reacquisition is further challenged by specular highlights, halo-like blooming, and reflective background clutter, where SMRs may appear as minute bright spots with ambiguous boundaries. In this paper, we propose RBD-YOLOv10n, a lightweight detector tailored for SMRs based on the YOLOv10 framework. To improve robustness while keeping deployment efficient, we introduce three lightweight enhancements across the backbone, neck, and head, including RepNMSC, W-BiFPN, and DEHead. Validated on a custom SMR dataset, our method achieves an [email protected] of 93.24% and an [email protected]:0.95 of 78.45%. Notably, the model is extremely lightweight, with 1.98M parameters and a 4.30 MB weight file (stored in FP16). These results show that the proposed method outperforms representative baseline detectors in balancing accuracy and efficiency, supporting practical high-precision LT vision-based SMR reacquisition under industrial conditions.

Applied SciencesVol. 16(6)
University of Science and Technology Beijing (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 24%
Robotics and Sensor-Based Localization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.