We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of bounding box priors over different aspect ratios and scales per feature map location. At prediction time, the network generates confidences that each prior corresponds to objects of interest and produces adjustments to the prior to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that requires object proposals, such as R-CNN and MultiBox, because it completely discards the proposal generation step and encapsulates all the computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on ILSVRC DET and PASCAL VOC dataset confirm that SSD has comparable performance with methods that utilize an additional object proposal step and yet is 100-1000x faster. Compared to other single stage methods, SSD has similar or better performance, while providing a unified framework for both training and inference.
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