Imagen de Portada

Computer Vision based Automated Timber Structural Defect Detection Framework

Guardado en:
Publicado en:ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 1387-1395
Autor principal: Rasool, Afia
Otros Autores: Mei, Qipei, Ahmad, Rafiq
Publicado:
IAARC Publications
Materias:
Wooden structures
Computer vision
Timber construction
Resource utilization
Defects
Wood construction
Construction industry
Accuracy
Sustainable materials
Datasets
Deep learning
Data collection
Algorithms
Manufacturing
Automation
Localization
Robotics
Acceso en línea:Citation/Abstract
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:In the wood construction industry, timber structural defect detection is usually considered a premanufacturing inspection step done manually. To address this issue, the proposed study discusses the timber structural defect detection method based on YOLOVS variants. The evaluation matrices used are precision, recall, mAP.5, and mAP.5-.95, and the results indicate stable convergence and consistent accuracy on the complex dataset instances. This research contributes to the automation of timber defect detection for precise and robust manufacturing of timber structures. The proposed method further improves resource utilization and contributes towards eliminating waste in the residential construction industry.
Fuente:Advanced Technologies & Aerospace Database