Deep learning applied to equipment detection on flat roofs in images captured by UAV

Maintenance on flat roofs is a complex activity. Equipment improperly positioned on flat roofs hinders the correct drainage of water and makes maintenance services more difficult. This article presents an experiment with deep learning algorithms involving 330 images acquired in 9 buildings by Unmanned Aerial Vehicle-UAV. This dataset was created by the authors to optimize decision-making for maintenance through automated processes and is being used for the first time in this article. The dataset refers to condenser equipment positioned on flat roofs and was tested in six state-of-the-art object-detection deep learning algorithms: Region-based convolutional neural networks (Faster R-CNN), Focal Loss (Retina-Net), Adaptive Training Sample Selection (ATSS), VarifocalNet (Vfnet), Side-Aware Boundary Localization (SABL) and FoveaBox (Fovea). Nine performance metrics were applied, achieving successful results by Faster R-CNN (Recall=0.93, Fscore=0.93, MAE=0.43) followed by ATSS (Precision=0.95). In a system with many variables, the target is the identification of the best algorithm capable of solving the proposed problem. In conclusion, the types of errors analyzed in detection alert to the diversity of causes related to the inherent characteristics of flat roofs that induce network confusion. •This is the first-time methods of Deep Learning for the Detection of Equipment Positioned on Flat Roofs were evaluated.•Six state-of-the-art object-detection deep learning algorithms were evaluated by nine performance metrics.•A new dataset was built with images of condensers of air conditioning equipment positioned on flat roofs.•Deep learning algorithms capable of differentiating similar objects were used in complex environments with many variables.•Identity and analysis of the causes that induce the network confusion was a contribution to improving automated processes.