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…
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Taxonomy
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