Print Email Facebook Twitter Automatic detection of bulldozer-induced changes on a sandy beach from video using YOLO algorithm Title Automatic detection of bulldozer-induced changes on a sandy beach from video using YOLO algorithm Author Barbero García, I. (University of Salamanca) Kuschnerus, M. (TU Delft Optical and Laser Remote Sensing) Vos, S.E. (TU Delft Coastal Engineering) Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing) Date 2023 Abstract Sandy beaches are subject to changes due to multiple factors, that are both natural (e.g. storms) and anthropogenic. Great efforts are being made to monitor these ecosystems and understand their dynamics in order to assure their conservation. The identification of anthropogenic changes and its differentiation from natural ones is an important task for coastal monitoring. In this study, we present a methodology for the detection of anthropogenic changes in a coastal ecosystem by automatically detecting active bulldozers in continuous beach video data. PCA is used to highlight changes in consecutive images due to moving objects. Next, the YOLO object detection algorithm is used to identify the bulldozers in the change images. YOLO was specifically trained for the task, obtaining a precision of 0.94 and a recall of 0.81. An automatic tool was developed, and the process was carried out on two months of video data, consisting of approximately 19 000 images. The resulting information was compared with changes derived from 3D data obtained from a permanent laser scanner. The correlation among the results of the two methodologies was computed. For a validation area and daily time frame a correlation of 0.88 was obtained between the number of detected bulldozers and the area affected by changes in height larger than 0.3 m. Subject Anthropogenic changesCoastal monitoringObject detectionPrincipal components analysis To reference this document use: http://resolver.tudelft.nl/uuid:1acc2768-ff27-47a0-8057-fcd609f0d55b DOI https://doi.org/10.1016/j.jag.2023.103185 ISSN 1569-8432 Source International Journal of Applied Earth Observation and Geoinformation, 117 Part of collection Institutional Repository Document type journal article Rights © 2023 I. Barbero García, M. Kuschnerus, S.E. Vos, R.C. Lindenbergh Files PDF 1_s2.0_S1569843223000079_main.pdf 11.83 MB Close viewer /islandora/object/uuid:1acc2768-ff27-47a0-8057-fcd609f0d55b/datastream/OBJ/view