Abstract:
Sri Lanka has a large number of snake species. The identification of the snake type is
a vital yet challenging task because of its shape, skin colour, and the environmental
background. Difficulty to identify snakes from the visible characteristics is a major
cause of deaths. The identification of snake types is important for the diagnosis process
to reduce the number of deaths and to avoid people from unnecessarily killing harmless
snakes which ensures the biodiversity that enriches nature. The purpose of this study
is to provide a model to classify snake images in different brightness levels which were
taken in day and night time. The Convolutional Neural Networks (CNNs) have been
used for snake detection and classification in a number of research. The CNN technique
with transfer learning for object recognition is used to train the model on a large dataset
and transfer its knowledge to a smaller dataset. In the earliest phase of this research, the
VGG-16 CNN model was used to train the model which provided a training accuracy
of 92.82% and a validation accuracy of 87.78% as a result for the same data set. The
pretrained VGG-19 CNN model consists of 19 layers used with Adam optimizer in
this study to achieve a higher accuracy. Different brightness level 2500 images of five
most venomous snake species in Sri Lanka such as Cobra, Sri Lankan Krait, Russel’s
Viper, Green Pit Viper and Hump-nosed Viper were used with enhancement techniques
for the detection and classification. The VGG-19 achieved 95.7% accuracy in training
92.6% accuracy in validation for different brightness level images including day and
night time. For the different brightness level images, the VGG -19 CNN model provided
a higher accuracy than the VGG-16 model.
