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2016 | OriginalPaper | Buchkapitel

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

verfasst von : Zhaowei Cai, Quanfu Fan, Rogerio S. Feris, Nuno Vasconcelos

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network. In the proposal sub-network, detection is performed at multiple output layers, so that receptive fields match objects of different scales. These complementary scale-specific detectors are combined to produce a strong multi-scale object detector. The unified network is learned end-to-end, by optimizing a multi-task loss. Feature upsampling by deconvolution is also explored, as an alternative to input upsampling, to reduce the memory and computation costs. State-of-the-art object detection performance, at up to 15 fps, is reported on datasets, such as KITTI and Caltech, containing a substantial number of small objects.

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Vorheriges Kapitel Joint Learning of Semantic and Latent Attributes

Nächstes Kapitel Deep Specialized Network for Illuminant Estimation

http://www.cvlibs.net/datasets/kitti/.

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Titel: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
verfasst von: Zhaowei Cai
Quanfu Fan
Rogerio S. Feris
Nuno Vasconcelos
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46492-3

Electronic ISBN: 978-3-319-46493-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46493-0_22

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