ArticleScientific reports2025
Effectiveness of traditional augmentation methods for rebar counting using UAV imagery with Faster R-CNN and YOLOv10-based transformer architectures.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A Range-Aware Attention Framework for Meteorological Visibility Estimation.Sensors (Basel, Switzerland) · 2026Article
- UAV-Based Oil Leakage Spot Detection Under Complex Illumination via a Collaborative Low-Light Enhancement and Detection Framework.Sensors (Basel, Switzerland) · 2026Article
- ATDIOU: Arctangent Differential Loss Function for Bounding Box Regression.Sensors (Basel, Switzerland) · 2026Article
- BWD-DETR: A Robust Framework for Bright-Field Wafer Defect Detection.Sensors (Basel, Switzerland) · 2026Article
- Robust Tracker: Integrating CPM-YOLO and BOTSORT for Cross-Modal Vessel Tracking.Sensors (Basel, Switzerland) · 2026Article
- VTC-Net: A Semantic Segmentation Network for Ore Particles Integrating Transformer and Convolutional Block Attention Module (CBAM).Sensors (Basel, Switzerland) · 2026Article
- Domain-adaptive faster R-CNN for non-PPE identification on construction sites from body-worn and general images.Scientific reports · 2026Article
- PBZGNet: A Novel Defect Detection Network for Substation Equipment Based on Gradual Parallel Branch Architecture.Sensors (Basel, Switzerland) · 2026Article
- Domain adaptation using transformer models for automated detection of exterior cladding materials in street view images.Scientific reports · 2025Article
- Development of an automated transformer-based text analysis framework for monitoring fire door defects in buildings.Scientific reports · 2025Article
- Fault-class coverage-aligned combined training for AFDD of AHUs across multiple buildings.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate inspection of Reinforced Concrete (RC) structures requires precise rebar counting. Although deep-learning object detectors can extract this information from drone imagery, their effectiveness depends on large, diverse, and well-labeled datasets. Image augmentation can increase data variability, yet its impact on Unmanned Aerial Vehicles (UAVs)-based rebar counting has been underexplored. This study systematically evaluates ten augmentation methods-brightness, contrast, perspective, rotation, scale, shearing, translation, blurring, a probabilistic sampling policy, and a sum of techniques composition-using Faster R-CNN and YOLOv10 across six backbones (ResNet-101, ResNet-152, MobileNetV3; ViT, PVT, Swin Transformer). Performance is reported using AP50, AP50:95, and exact-count accuracy. Results show that augmentation efficacy is both architecture and metric-dependent. The best test-set configuration is YOLOv10-PVT with shearing, which achieves AP50 = 87.71%, AP50:95 = 68.53%, and rebar-count accuracy = 86.27%-improvements of + 5.92, + 9.07, and + 5.99 percentage points, respectively, over the PVT original baseline. A probabilistic sampling policy provides consistent, policy-level gains over original data and approaches the best single transform (especially with a magnitude ramp), whereas indiscriminate a sum of techniques application does not reliably outperform the top single augmentation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.