  This paper proposes an innovative object detector by leveraging deep features
learned in high-level layers. Compared with features produced in earlier
layers, the deep features are better at expressing semantic and contextual
information. The proposed deep feature learning scheme shifts the focus from
concrete features with details to abstract ones with semantic information. It
considers not only individual objects and local contexts but also their
relationships by building a multi-scale deep feature learning network (MDFN).
MDFN efficiently detects the objects by introducing information square and
cubic inception modules into the high-level layers, which employs
parameter-sharing to enhance the computational efficiency. MDFN provides a
multi-scale object detector by integrating multi-box, multi-scale and
multi-level technologies. Although MDFN employs a simple framework with a
relatively small base network (VGG-16), it achieves better or competitive
detection results than those with a macro hierarchical structure that is either
very deep or very wide for stronger ability of feature extraction. The proposed
technique is evaluated extensively on KITTI, PASCAL VOC, and COCO datasets,
which achieves the best results on KITTI and leading performance on PASCAL VOC
and COCO. This study reveals that deep features provide prominent semantic
information and a variety of contextual contents, which contribute to its
superior performance in detecting small or occluded objects. In addition, the
MDFN model is computationally efficient, making a good trade-off between the
accuracy and speed.
