Amanda Hodgson

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Monitoring and estimation of marine populations is of paramount importance for the conservation and management of sea species. Regular surveys are used to this purpose followed often by a manual counting process. This paper proposes an algorithm for automatic detection of dugongs from imagery taken in aerial surveys. Our algorithm exploits the fact that dugongs are rare in most images, therefore we determine regions of interest partially based on color rarity. This simple observation makes the system robust to changes in illumination. We also show that by applying the extended-maxima transform on red-ratio images, submerged dugongs with very fuzzy edges can be detected. Performance figures obtained here are promising in terms of degree of confidence in the detection of marine species, but more importantly our approach represents a significant step in automating this type of surveys.

This paper presents two algorithms to automate the detection of marine species in aerial imagery. An algorithm from an initial pilot study is presented in which morphology operations and colour analysis formed the basis of its working principle. A second approach is presented in which saturation channel and histogram-based shape profiling were used. We report on performance for both algorithms using datasets collected from an unmanned aerial system at an altitude of 1000ft. Early results have demonstrated recall values of 48.57% and 51.4%, and precision values of 4.01% and 4.97%.