Abstract:
This research work investigates the possibility of using deep learning techniques to identify
snakes using images. There are 103 snake species found in Sri Lanka. Most of them share
common attributes, body shape, head shape, color-patterns, color patches, and other physical
attributes. Therefore, it is difficult to identify these snakes separately. This research proposes
to use deep learning as an approach to differentiate various types of snakes. Nowadays Deep
Neural Networks have become a popular technique for image recognition. Generally, large
numbers of example images are used as inputs to these convolutional neural networks. 1500
images of four highly venomous snake species – Indian Cobra, Russell’s Viper, Common Krait,
Saw Scaled Viper and moderately venomous Hump Nosed Viper, of each were collected. Next,
those images were labelled into five classes and were separated into training and validation
sets. The CNN contained five hidden layers and two fully connected layers. The hidden layers
consisted of convolution layers and pooling layers. Convolutional Layers extracted features of
the input image by using a filter (kernel). A filter was a matrix consists with weight values.
Convolution preserved the relationship between pixels by learning image features and produce
feature maps. Rectified Linear Unit - ReLU was used as the activation function in order to make
the output non-linear. Pooling Layers reduced the special size of the output through replacing
values in the kernel by a function of those values. Fully Connected Layers were used to flatten
the input representation into a feature vector to predict the probabilities of outputs. The model
was tested using data sets which contained both real-size and re-sized images and achieved satisfactory results. In all tests, the model achieved training accuracy rates and validation accuracy
rates over 0.98. However, the main limitation of the proposed model is the limited scope. The
model can identify the most venomous snakes but it fails to consider the other snakes is a major
hindrance for practical use. Therefore, the main future development is identified as expansion
of the number of snakes it can identify
