Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas

Drones have recently been used to assess wildlife populations and their abundance. The automatic detection of target animals in drone footage enables efficient abundance estimation. However, accurately detecting animals remains challenging, especially in complex field environments. Moreover, automating the tracking of individuals across consecutive images and counting them along transect lines is necessary to apply drones to line-transect surveys. In this dataset, we prepared images obtained at Japanese coastal areas from drones for deep-learning-based models to automatically detect green turtles (Chelonia mydas). The images contain training and validation data for the YOLOv7 model. In addition, for testing the BoT-SORT object-tracking algorithm to track green turtles, we prepared eight drone footage clips., , , # Drone images and footage for green turtle detection and tracking

We have submitted our training images (**train folder**), validation images (**valid folder**), and drone footage data for testing (**video folder**).

## Description of the data and file structure

#### File: data.7z

**Description:** 

* *train* folder: images used for training of the YOLOv7 model for green turtle detection
* *valid* folder: images used for validation of the YOLOv7 model for green turtle detection
* *video* folder: drone footage data used for testing of the BoT-SORT model for green turtle tracking and counting

See our primary article at [https://doi.org/10.1016/j.ecoinf.2025.103009](https://doi.org/10.1016/j.ecoinf.2025.103009 \"Persistent link using digital object identifier\") for details