  For most of the object detectors based on multi-scale feature maps, the
shallow layers are rich in fine spatial information and thus mainly responsible
for small object detection. The performance of small object detection, however,
is still less than satisfactory because of the deficiency of semantic
information on shallow feature maps. In this paper, we design a Multi-scale
Deconvolutional Single Shot Detector (MDSSD), especially for small object
detection. In MDSSD, multiple high-level feature maps at different scales are
upsampled simultaneously to increase the spatial resolution. Afterwards, we
implement the skip connections with low-level feature maps via Fusion Block.
The fusion feature maps, named Fusion Module, are of strong feature
representational power of small instances. It is noteworthy that these
high-level feature maps utilized in Fusion Block preserve both strong semantic
information and some fine details of small instances, rather than the top-most
layer where the representation of fine details for small objects are
potentially wiped out. The proposed framework achieves 77.6% mAP for small
object detection on the challenging dataset TT100K with 512 x 512 input,
outperforming other detectors with a large margin. Moreover, it can also
achieve state-of-the-art results for general object detection on PASCAL VOC2007
test and MS COCO test-dev2015, especially achieving 2 to 5 points improvement
on small object categories.
