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

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.

Automated summary

Efforts are being made to monitor and understand the dynamics of sandy beaches, as they are subject to changes from natural and anthropogenic factors. This study presents a methodology for detecting anthropogenic changes in coastal ecosystems by automatically identifying active bulldozers in beach video data. By using PCA and the YOLO algorithm, the bulldozers in change images can be accurately detected. The correlation between the results of this methodology and 3D data obtained from a laser scanner was computed for validation.