DOI: 10.5061/dryad.s4mw6m98n
title: Identification of free-ranging mugger crocodiles by applying deep learning methods on UAV imagery

description: Individual identification contributes significantly towards investigating
 behavioral mechanisms of animals and understanding underlying ecological
 principles. Most studies employ invasive procedures for individually
 identifying organisms. In recent times, computer-vision techniques have
 served as an alternative to invasive methods. However, these studies
 primarily rely on user input data collected from captivity or from
 individuals under partially restrained conditions. Challenges in
 collecting data from free-ranging individuals are higher when compared to
 captive populations. However, the former is a far more important priority
 for real-world applications. In this paper, we used UAV to collect data
 from free-ranging mugger crocodiles Crocodylus palustris. We applied
 convolutional neural networks (CNNs) to individually identify muggers
 based on their dorsal scute patterns. The CNN model was trained on a data
 set of 88,000 images focusing on the mugger’s dorsal body. The data was
 collected from 143 individuals across 19 different locations along the
 western part of India. We trained two CNN models, one with an annotated
 bounding box approach, the YOLO-v5l, and another without annotations, the
 Inception-v3. We used two parameters, True Positive Rate (TPR) and True
 Negative Rate (TNR), to validate the efficiency of the trained models.
 Using YOLO-v5l, TPR (re-identification of trained muggers) and TNR
 (differentiating untrained muggers as 'unknown') values at 0.84
 threshold were 88.8% and 89.6%, respectively. The trained model showed
 100% TNR for the non-mugger species, the Gharial Gavialis gangeticus, and
 the Saltwater crocodile Crocodylus porosus. The performance of the CNN
 model was reliable and accurate while using only 125 images per individual
 for training purposes. Inception-v3 underperformed for both the
 parameters, thus, showing that a bounding box approach (YOLO-v5l model)
 with background elimination is a promising method to individually identify
 free-ranging mugger crocodiles. Our manuscript demonstrates that UAV
 imagery appears to be a promising tool for non-invasive collection of data
 from free-ranging populations. It can be used to train open-source
 algorithms for individual identification. Further, the identification
 method is entirely based upon dorsal scute patterns, which can be applied
 to different crocodilian species, as well.
description: <strong>Folder 1: Maps and information on crocodile
 classes</strong> The folder includes generated
 GIS maps and respective shape files that were used on the
 Quantum-Geographic Information System (QGIS). The folder contains GPS data
 on sampling locations and information (date and collection site details)
 on sampled classes (individuals) of crocodiles.  For use in the QGIS, all
 XML files were converted to text files, and we have those files, as well. 
 Following is a navigation map to access information within Folder
 1. <strong>Folder 2: Code files</strong> The folder contains code files for data-preprocessing, Inception-v3 model, and YOLO-v5l model. The folder contains all the necessary files required and should be downloaded as it is. All Jupyter notebooks and their corresponding codes use relative paths. More details regarding individual files and folders are available in README.txt files in respective folders. The folder and its subfolders are as per the folder map below.   <strong>Folder 3: Open set test data package</strong> It includes two folders, Test_I and Test_II. Test_I contains crocodiles’ images that were used to check the True Positive Rate of the model and Test_II images were used to check the True Negative Rate of the model. The pre-trained model saw both the folders (all the images) together to simulate ‘open set’ situation. The details of the images are given in the folder map below.     <strong>Folder 4: Supplementary results</strong> The folder includes all the results of training, validation, and testing for the Inception-v3 model and YOLO-v5l model, using 90:10 data splits for all three methods, I, II, and III.  Note: The entire imagery data used for training the model is available with the corresponding author and can be made available for research purposes upon request.