AbdulWahab Kabani and Mahmoud R. El-Sakka, "North
Atlantic Right Whale Localization and Recognition Using
Very Deep and Leaky Neural Network"
Mathematics for Applications Journal,
Vol. 5, No. 2, pp. 155-170, 2016.
Abstract
We describe a deep learning model that can be used to
recognize individual right whales in aerial images. We
developed our model using a data set provided by the National
Oceanic and Atmospheric Administration. The main challenge we
faced when working on this data set is that the size of the
training set is very small (4,544 images) with some classes
having only 1 image. While this data set is by far the
largest of its kind, it is very difficult to train a deep
neural network with such a small data set. However, we were
able to overcome this challenge by dividing this problem into
smaller tasks and by reducing the viewpoint variance in the
data set. First, we localize the body and the head of the
whale using deep learning. Then, we align the whale and
normalize it with respect to rotation. Finally, a network is
used to recognize the whale by analyzing its callosities. The
top-1 accuracy of the model is 69.7% and the top-5 accuracy
is 85%. The solution we describe in this paper was ranked 5th
(out of 364 teams) in a challenge to solve this problem.