Identification of free-ranging mugger crocodiles by applying deep learning methods on UAV imagery

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.