A carregar...
Publicação
Weighted adaptive active transfer learning for imbalanced multi-object classification in construction site imagery
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 8.48 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Construction site monitoring relies on robust image classification to enhance safety, track progress, and optimize resource management. However, the amount of clutter and the high cost of manual labeling pose significant challenges. This paper presents an approach to multi-object classification in construction sites using Adaptive Active Transfer Learning. The Weighted Active Transfer Learning with Adaptive Sampling (WATLAS) framework is introduced, where Transfer Learning is combined with weighted Active Learning to efficiently classify diverse objects. A pre-trained InceptionV3 architecture integrated with bidirectional long short-term memory (BiLSTM) layers is utilized, and superior performance is achieved through adaptive sampling techniques compared to traditional methods. WATLAS achieves 97 % accuracy on a comprehensive dataset of 9344 construction site images spanning 15 object categories and maintaining 90 % accuracy with only 5 % labeled data. By optimizing performance metrics, the framework demonstrates significant improvements over traditional methods, making it a scalable solution for construction site monitoring.
Descrição
Mannem, K. R., Prieto, S. A., Soto, B. G. D., & Bação, F. (2025). Weighted adaptive active transfer learning for imbalanced multi-object classification in construction site imagery. Automation In Construction, 176, Article 106297. https://doi.org/10.1016/j.autcon.2025.106297 --- This research benefited from utilizing resources available at the Core Technology Platforms (CTP) of New York University Abu Dhabi (NYUAD). In particular, the algorithms developed in this study used the research computing services at NYUAD's Center for Research Computing and High-Performance Computing (HPC). This research was partially supported by different Centers at NYUAD. In particular, the Center for Sand Hazards and Opportunities for Resilience, Energy, and Sustainability (SHORES) funded by Tamkeen under the NYUAD Research Institute Award CG013, the Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001, and the Center for Artificial Intelligence and Robotics (CAIR), funded by Tamkeen under the NYUAD Research Institute Award CG010. This work was also supported by national funds through FCT (Foundation for Science and Technology), under the project - UIDB/04152 - Information Management Research Centre (MagIC)/NOVA IMS.
Palavras-chave
InceptionV3 BiLSTM WATLAS Active learning Transfer learning Adaptive sampling Control and Systems Engineering Civil and Structural Engineering Building and Construction