Non-Invasive Monitoring of Dense Landes Goose Flocks: An Automated Counting Method Driven by Simulation Data and Robust Trajectory Anchoring

To address low efficiency in manual counting and high costs of data collection and labeling in Landes goose farming, this study developed a simulation-driven video detection and counting method. A three-dimensional simulation environment generated goose scenes under varied densities, perspectives, and lighting conditions, with automatic bounding-box labeling. A YOLO26 detector was pre-trained on the 6000-image training subset of a 10,000-image simulated dataset, which was divided into training, validation, and test subsets at a ratio of 6:2:2 (6000, 2000, and 2000 images, respectively), and was then fine-tuned using 500 real images. A bottom-center trajectory anchor and line-crossing rules were combined with BoT-SORT tracking to construct a detection-tracking-counting pipeline. Evaluation on three real video sequences comprising 1780 reference crossing events yielded an event-level counting accuracy of 99.16% and an aggregate net-count accuracy of 99.38%, with a mean absolute error of 3.7 and a root mean square error of 5.3 across the sequences. In the dense sequence, the predicted count was 1021 against 1030 reference crossings, corresponding to a net-count accuracy of 99.13%. These findings support simulation-assisted training and non-contact line-crossing counting for the evaluated Landes goose passage. Further validation is needed before extending the method to other farm layouts or production environments.