20. a unified multi-scale deep convolutional neural network for fast object detection

Department: Electrical & Computer Engineering
Research Institute Affiliation: Agile Center for Visual Computing
Faculty Advisor(s): Nuno M. Vasconcelos

Primary Student
Name: Zhaowei Cai
Email: zhc003@ucsd.edu
Phone: 858-534-4538
Grad Year: 2019

Abstract
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MSCNN 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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