  We define the object detection from imagery problem as estimating a very
large but extremely sparse bounding box dependent probability distribution.
Subsequently we identify a sparse distribution estimation scheme, Directed
Sparse Sampling, and employ it in a single end-to-end CNN based detection
model. This methodology extends and formalizes previous state-of-the-art
detection models with an additional emphasis on high evaluation rates and
reduced manual engineering. We introduce two novelties, a corner based
region-of-interest estimator and a deconvolution based CNN model. The resulting
model is scene adaptive, does not require manually defined reference bounding
boxes and produces highly competitive results on MSCOCO, Pascal VOC 2007 and
Pascal VOC 2012 with real-time evaluation rates. Further analysis suggests our
model performs particularly well when finegrained object localization is
desirable. We argue that this advantage stems from the significantly larger set
of available regions-of-interest relative to other methods. Source-code is
available from: https://github.com/lachlants/denet
