Automatic detection of bulldozer-induced changes on a sandy beach from video using YOLO algorithm

Inés Barbero-García*, Mieke Kuschnerus, Sander Vos, Roderik Lindenbergh

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
46 Downloads (Pure)

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.

Original languageEnglish
Article number103185
Number of pages11
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume117
DOIs
Publication statusPublished - 2023

Keywords

  • Anthropogenic changes
  • Coastal monitoring
  • Object detection
  • Principal components analysis

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