Change Detection in Unmanned Aerial Vehicle Images for Progress Monitoring of Road Construction
Auteur(s): |
Dongyeob Han
Suk Bae Lee Mihwa Song Jun Sang Cho |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 5 avril 2021, n. 4, v. 11 |
Page(s): | 150 |
DOI: | 10.3390/buildings11040150 |
Abstrait: |
Currently, unmanned aerial vehicles are increasingly being used in various construction projects such as housing developments, road construction, and bridge maintenance. If a drone is used at a road construction site, elevation information and orthoimages can be generated to acquire the construction status quantitatively. However, the detection of detailed changes in the site owing to construction depends on visual video interpretation. This study develops a method for automatic detection of the construction area using multitemporal images and a deep learning method. First, a deep learning model was trained using images of the changing area as reference. Second, we obtained an effective application method by applying various parameters to the deep learning process. The application of the time-series images of a construction site to the selected deep learning model enabled more effective identification of the changed areas than the existing pixel-based change detection. The proposed method is expected to be very helpful in construction management by aiding in the development of smart construction technology. |
Copyright: | © 2021 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10602569 - Publié(e) le:
17.04.2021 - Modifié(e) le:
02.06.2021