Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Santos, R (Author) Other; Ribeiro, D (Author) Other; Lopes, P (Author) Other; Cabral, R (Author) Other; Rui Calçada (Author) FEUP

Journal

Title: Automation in Construction

Vol. 139

ISSN: 0926-5805

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00W-J1V

DOI: 10.1016/j.autcon.2022.104324

Abstract (EN): In recent years deep-learning techniques have been developed and applied to inspect cracks in RC structures. The accuracy of these techniques leads to believe that they may also be applied to the identification of other pathologies. This article proposes a technique for automated detection of exposed steel rebars. The tools developed rely on convolutional neural networks (CNNs) based on transfer-learning using AlexNet. Experiments were conducted in large-scale structures to assess the efficiency of the method. To circumvent limitations on the proximity access to structures as large as the ones used in the experiments, as well as increase cost efficiency, the image capture was performed using an unmanned aerial system (UAS). The final goal of the proposed methodology is to generate orthomosaic maps of the pathologies or structure 3D models with superimposed pathologies. The results obtained are promising, confirming the high adaptability of CNN based methodologies for structural inspection.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17