  The main contribution of this paper is an approach for introducing additional
context into state-of-the-art general object detection. To achieve this we
first combine a state-of-the-art classifier (Residual-101[14]) with a fast
detection framework (SSD[18]). We then augment SSD+Residual-101 with
deconvolution layers to introduce additional large-scale context in object
detection and improve accuracy, especially for small objects, calling our
resulting system DSSD for deconvolutional single shot detector. While these two
contributions are easily described at a high-level, a naive implementation does
not succeed. Instead we show that carefully adding additional stages of learned
transformations, specifically a module for feed-forward connections in
deconvolution and a new output module, enables this new approach and forms a
potential way forward for further detection research. Results are shown on both
PASCAL VOC and COCO detection. Our DSSD with $513 \times 513$ input achieves
81.5% mAP on VOC2007 test, 80.0% mAP on VOC2012 test, and 33.2% mAP on COCO,
outperforming a state-of-the-art method R-FCN[3] on each dataset.
