Towards Construction Progress Estimation Based on Images Captured on Site

Publication

Peter Hevesi, Ramprasad Chinnaswamy Devaraj, Matthias Tschöpe, Oliver Petter, Janis Nikolaus Elfert, Vitor Fortes Rey, Marco Hirsch, Paul Lukowicz

In: EAI IndustrialIoT 2020 - 4th EAI International Conference on Industrial IoT Technologies and Applications. EAI International Conference on Industrial IoT Technologies and Applications (EAI IndustrialIoT-2020) December 11 Online-Conference Springer 2020.

Abstract

State of the art internet of things (IoT) and mobile moni- toring systems promise to help gathering real time progress information from construction sites. However, on remote sites the adaptation of those technologies is frequently difficult due to a lack of infrastructure and often harsh and dynamic environments. On the other hand, visual inspection by experts usually allows a quick assessment of a project’s state. In some fields, drones are already commonly used to capture aerial footage for the purpose of state estimation by domain experts. We propose a two-stage model for progress estimation leveraging im- ages taken at the site. Stage 1 is dedicated to extract possible visual cues, like vehicles and resources. Stage 2 is trained to map the visual cues to specific project states. Compared to an end-to-end learning task, we intend to have an interpretable representation after the first stage (e.g. what objects are present, or later what are their relationships (spa- tial/semantic)). We evaluated possible methods for the pipeline in two use-case scenarios - (1) road and (2) wind turbine construction. We evaluated methods like YOLOv3-SPP for object detection, and com- pared various methods for image segmentation, like Encoder-Decoder, DeepLab V3, etc. For the progress state estimation a simple decision tree classifier was used in both scenarios. Finally, we tested progress es- timation by a sentence classification network based on provided free-text image descriptions.

Projekte

ConWearDi - Digitalisierung von Baudienstleistungen und -prozessen mit Industrie 4.0 Technologien - Construction - Wearables - Digitization

Towards Construction Progress Estimation Based on Images Captured on Site

Abstract

Projekte

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