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

Cai, Zhaowei ; Fan, Quanfu ; Feris, Rogerio S. ; Vasconcelos, Nuno

Computer Vision – ECCV 2016, p.354-370 [Periódico revisado por pares]

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Título:
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Autor: Cai, Zhaowei ; Fan, Quanfu ; Feris, Rogerio S. ; Vasconcelos, Nuno
Assuntos: Multi-scale ; Object detection ; Unified neural network
É parte de: Computer Vision – ECCV 2016, p.354-370
Descrição: 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.
Títulos relacionados: Lecture Notes in Computer Science
Editor: Cham: Springer International Publishing
Idioma: Inglês

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