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-ofthe-art classifier ) with a fast detection framework (SSD [18]). We then augment SSD+Residual-101 with deconvolution layers to introduce additional largescale 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 × 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.
In order to improve the accuracy, DSSD REF suggests to augment SSD+ResNet-101 with deconvolution layers to introduce additional larges-scale context.
DSSD : Deconvolutional Single Shot Detector
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Computer Science