SSD: Single Shot MultiBox Detector

TL;DR

SSD is a fast, accurate single-shot object detection method that eliminates proposal generation, combining multi-scale predictions in a single neural network for real-time applications.

Contribution

The paper introduces SSD, a novel single-shot detection framework that achieves high accuracy and speed without requiring object proposals or additional stages.

Findings

Achieves 72.1% mAP on VOC2007 at 58 FPS with 300x300 input.

Outperforms Faster R-CNN in accuracy at comparable speeds.

Simplifies detection pipeline by integrating all computations into one network.

Abstract

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 default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box 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 require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all 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 the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For $300\times 300$ input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for $500\times 500$ input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model. Code is available at https://github.com/weiliu89/caffe/tree/ssd .