  The ability to accurately detect and classify objects at varying pixel sizes
in cluttered scenes is crucial to many Navy applications. However, detection
performance of existing state-of the-art approaches such as convolutional
neural networks (CNNs) degrade and suffer when applied to such cluttered and
multi-object detection tasks. We conjecture that spatial relationships between
objects in an image could be exploited to significantly improve detection
accuracy, an approach that had not yet been considered by any existing
techniques (to the best of our knowledge) at the time the research was
conducted. We introduce a detection and classification technique called
Spatially Related Detection with Convolutional Neural Networks (SPARCNN) that
learns and exploits a probabilistic representation of inter-object spatial
configurations within images from training sets for more effective region
proposals to use with state-of-the-art CNNs. Our empirical evaluation of
SPARCNN on the VOC 2007 dataset shows that it increases classification accuracy
by 8% when compared to a region proposal technique that does not exploit
spatial relations. More importantly, we obtained a higher performance boost of
18.8% when task difficulty in the test set is increased by including highly
obscured objects and increased image clutter.
