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SSD: Single Shot MultiBox Detector

SSD: Single Shot MultiBox DetectorWei Liu1, Dragomir Anguelov2, Dumitru Erhan3, Christian Szegedy3,Scott Reed4, Cheng-Yang Fu1, Alexander C. Berg11 UNC Chapel Hill2 Zoox of Michigan, present a method for detecting objects in images using a singledeep neural network. Our approach, named SSD, discretizes the output space ofbounding boxes into a set of default boxes over different aspect ratios and scalesper feature map location. At prediction time, the network generates scores for thepresence of each object category in each default box and produces adjustments tothe box to better match the object shape. Additionally, the network combines pre-dictions from multiple feature maps with different resolutions to naturally handleobjects of various sizes.

ture resampling stage. We are not the first to do this (cf [4,5]), but by adding a series of improvements, we manage to increase the accuracy significantly over previous at-tempts. Our improvements include using a small convolutional filter to predict object categories and offsets in bounding box locations, using separate predictors ...

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