Peer-reviewedCC BY 4.0Scientific Reports
SSLD-DET: improving symbol detection in station signalling layout drawings with multi-scale feature fusion and advanced attention mechanisms
Related
- Improved YOLOv7-based surface defect detection method incorporating feature retention and densely-scale spatial attention mechanisms
- PatentOCR: A Benchmark for Fine-Grained Detection and OCR of Component Identifiers in Patent Drawings
- Lightweight YOLOv8n marigold corolla detection and picking-point localization method based on ECR progressive channel compression and dual attention mechanisms
- Network representations of digital clock drawings for cognitive impairment detection
- Improving explainability of attention-based deep learning models for thermal analysis systems
BibTeX
@article{kuang2026sslddet,
title = {{SSLD-DET: improving symbol detection in station signalling layout drawings with multi-scale feature fusion and advanced attention mechanisms}},
author = {Wenzhen Kuang and Yunchen Hao},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-79115-8},
url = {https://www.nature.com/articles/s41598-026-79115-8},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SSLD-DET: improving symbol detection in station signalling layout drawings with multi-scale feature fusion and advanced attention mechanisms
AU - Wenzhen Kuang
AU - Yunchen Hao
PY - 2026
DA - 2026/11/09
JO - Scientific Reports
DO - 10.1038/s41598-026-79115-8
UR - https://www.nature.com/articles/s41598-026-79115-8
SN - 2045-2322
ER -