cpnlab/YOLOR-Streetlights

YOLOR-Streetlights

<table> <tr> <td width="30%" valign="top"> <img src="streetlight.png" alt="YOLOR-Streetlights — example streetlight detection" width="100%"> </td> <td valign="top">

YOLOR-Streetlights is a fine-tuned object detection model for BS identification for beam initialization to detect urban streetlight infrastructure in one inference pass. Data was collected on the [University of Nebraska–Lincoln](https://www.unl.edu/) campus.

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Reference implementation for the paper:

Avhishek Biswas\, Apala Pramanik\, Eylem Ekici, Mehmet C. Vuran. "Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity." (\*equal contribution) arXiv: <https://doi.org/10.48550/arXiv.2605.05071>

<p align="center"> <img src="overview2_updated.png" alt="VIBE five-stage camera-primed beam-management pipeline" width="90%"> </p>

Quick links

• —Paper (arXiv): <https://doi.org/10.48550/arXiv.2605.05071>
• —Code and Data: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice>
• —Training pipeline: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice/tree/main/YOLOR_Training>

Usage

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(repo_id="cpnlab/YOLOR-Streetlights", filename="last.pt")
model = YOLO(weights)
results = model.predict("path/to/image.jpg", conf=0.25)

Class indices: 0–79 = COCO; 80 = streetlight.

Training data

Code and Data: <https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice>

Citation

@inproceedings{biswas2026look,
  title     = {Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional
               mmWave Beam Management for Vehicular Connectivity},
  author    = {Biswas, Avhishek and Pramanik, Apala and Ekici, Eylem and Vuran, Mehmet C.},
  booktitle = {Proc. IEEE SECON},
  year      = {2026}
}

Paper: <https://doi.org/10.48550/arXiv.2605.05071>

Contact

For questions about this model or the paper, contact the corresponding authors:

• —Avhishek Biswas — abiswas3@huskers.unl.edu
• —Apala Pramanik — apramanik2@huskers.unl.edu

Acknowledgments

Developed at the [Cyber Physical Networking (CPN) Lab](https://cpn.unl.edu/), School of Computing, University of Nebraska–Lincoln, in collaboration with The Ohio State University. Thanks to Sivers Semiconductors, Ettus Research, and the open-source Ultralytics, PyTorch, and Ettus UHD communities.