Dokumentdetails
ID
oai:arXiv.org:2411.04475
Thema
Computer Science - Computer Vision...Autor
Phan, Trong-Nhan Nguyen, Hoang-Hai Ha, Thi-Thu-Hien Thai, Huy-Tan Le, Kim-HungKategorie
Computer Science
Jahr
2024
Auflistungsdatum
13.11.2024
Schlüsselwörter
process modelsZusammenfassung
Visual inspections of bridges are critical to ensure their safety and identify potential failures early.
This inspection process can be rapidly and accurately automated by using unmanned aerial vehicles (UAVs) integrated with deep learning models.
However, choosing an appropriate model that is lightweight enough to integrate into the UAV and fulfills the strict requirements for inference time and accuracy is challenging.
Therefore, our work contributes to the advancement of this model selection process by conducting a benchmark of 23 models belonging to the four newest YOLO variants (YOLOv5, YOLOv6, YOLOv7, YOLOv8) on COCO-Bridge-2021+, a dataset for bridge details detection.
Through comprehensive benchmarking, we identify YOLOv8n, YOLOv7tiny, YOLOv6m, and YOLOv6m6 as the models offering an optimal balance between accuracy and processing speed, with mAP@50 scores of 0.803, 0.837, 0.853, and 0.872, and inference times of 5.3ms, 7.5ms, 14.06ms, and 39.33ms, respectively.
Our findings accelerate the model selection process for UAVs, enabling more efficient and reliable bridge inspections.
Phan, Trong-Nhan,Nguyen, Hoang-Hai,Ha, Thi-Thu-Hien,Thai, Huy-Tan,Le, Kim-Hung, 2024, Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis
Performance Validity Testing on the NIH Toolbox Cognition Battery: Base Rates of Failed Embedded Validity Indicators in the Adult Normative Sample
crystallized data false-positive failure tests scores age-adjusted validity standard using evi failures multivariable nihtb-cb cognition rates sd
