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2018 | OriginalPaper | Buchkapitel

CornerNet: Detecting Objects as Paired Keypoints

verfasst von : Hei Law, Jia Deng

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.1% AP on MS COCO, outperforming all existing one-stage detectors.

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Titel: CornerNet: Detecting Objects as Paired Keypoints
verfasst von: Hei Law
Jia Deng
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01263-2

Electronic ISBN: 978-3-030-01264-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01264-9_45

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