Automated object detection based on YOLOv11 for monitoring benthic population dynamics: A new approach combining photogrammetry and open‐source GIS

Abstract

Benthic organisms, such as sea cucumbers, play a crucial role in delivering essential ecosystem services, including nutrient recycling through feeding, excretion and bioturbation processes. Considering recent harvest activities in subtidal zones worldwide, it is imperative to define rapid and cost‐effective solutions to improve monitoring tools. Large‐scale Structure from Motion (SfM) photogrammetric mapping was conducted along Mediterranean infralittoral sea bottoms to provide training and testing datasets for developing a new object detection model. Several high‐resolution (0.5 cm/pixel) orthophoto mosaics were used to train a new object detection model based on the YOLOv11 architecture. Subsequently, georeferenced imagery was used to assess the accuracy of manual counts compared to the outcomes produced by the model. The model exhibited strong performance on the validation set, achieving a precision of 83% and a recall of 88%. The implementation of the open‐source GIS model through the Deepness plugin in QGIS software enabled the rapid deployment of the model (15.9 ± 1.35 min) over large (5500 m ² ) orthophoto mosaics, revealing the effects of habitat and seasonality on the detection results. The length of the diagonal of the bounding boxes used as a proxy for the body length of sea cucumbers, combined with a Gaussian kernel density estimator analysis, revealed comparable results between the estimated and observed values of sea cucumbers, confirming the validity of the proposed method for extracting biologically relevant information for natural population monitoring. This research presented a new and scalable workflow for identifying small benthic organisms through underwater photogrammetric‐based imagery. The findings underscore the promise of using cost‐effective and open‐source object detection tools to provide a valuable tool for ecological assessments of natural populations. Data from automatic detection systems represent a promising non‐invasive approach for directly analysing species distribution patterns based solely on image data, marking a significant improvement in ecological monitoring techniques.