DSSD Deconvolutional Single Shot Detector
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.
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