MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

Yuanwei Wu, Wenchi Ma, Guanghui Wang

Abstract:

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, multiscale, 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.