  Object detection in challenging situations such as scale variation,
occlusion, and truncation depends not only on feature details but also on
contextual information. Most previous networks emphasize too much on detailed
feature extraction through deeper and wider networks, which may enhance the
accuracy of object detection to certain extent. However, the feature details
are easily being changed or washed out after passing through complicated
filtering structures. To better handle these challenges, the paper proposes a
novel framework, multi-scale, deep inception convolutional neural network
(MDCN), which focuses on wider and broader object regions by activating feature
maps produced in the deep part of the network. Instead of incepting inner
layers in the shallow part of the network, multi-scale inceptions are
introduced in the deep layers. The proposed framework integrates the contextual
information into the learning process through a single-shot network structure.
It is computational efficient and avoids the hard training problem of previous
macro feature extraction network designed for shallow layers. Extensive
experiments demonstrate the effectiveness and superior performance of MDCN over
the state-of-the-art models.
