Single-fusion detector: Towards faster multi-scale object detection

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

International Conference on Image Processing, ICIP

First Page

76

Last Page

80

Publication Date

2019

Abstract

Despite recent improvements, the arbitrary sizes of objects still impede the predictive ability of object detectors. Recent solutions combine feature maps of different receptive fields to detect multi-scale objects. However, these methods have large computational costs resulting to slower inference time, which is not practical for real-time applications. Contrarily, fusion methods depending on large networks with many skip connections demand larger memory requirement, prohibiting usage in devices with limited memory. In this paper, we propose a more computationally efficient fusion method which integrates higher-order information to low-level feature maps using a single operation. Our method can flexibly adapt to any base network, allowing tailored performance for different computational requirements. Our approach achieves 81.7% mAP at 41 FPS on the PASCAL VOC dataset using ResNet-50 as the base network, which is superior in terms of both speed and mAP as compared to several state-of-the-art baselines, even those which use larger base networks.

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Digitial Object Identifier (DOI)

10.1109/ICIP.2019.8802913

Disciplines

Computer Sciences

Keywords

Image converters; Neural networks (Computer science)

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