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Resumé

Object detection can be difficult due to challenges such as variations in objects both inter- and intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination strategies were explored for combining the individual member detections into an ensemble result, namely average and a weighted average. R-FCNs were trained and tested on the PASCAL VOC benchmark object detection dataset. Results proved positive with an increase in Average Precision (AP), compared to state-of-the-art similar systems, when ensemble members were combined appropriately.
OriginalsprogEngelsk
Titel International Joint Conference on Computational Intelligence
Vol/bind1
ForlagSCITEPRESS Digital Library
Publikationsdato2017
Sider110-120
ISBN (Trykt)978-989-758-274-5
DOI
StatusUdgivet - 2017
BegivenhedInternational Joint Conference on Computational Intelligence - Funchal, Portugal
Varighed: 1 nov. 20173 nov. 2017
Konferencens nummer: 9
http://www.ijcci.org/

Konference

KonferenceInternational Joint Conference on Computational Intelligence
Nummer9
LandPortugal
ByFunchal
Periode01/11/201703/11/2017
Internetadresse

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Citer dette

Rasmussen, C. B., Nasrollahi, K., & Moeslund, T. B. (2017). R-FCN Object Detection Ensemble based on Object Resolution and Image Quality. I International Joint Conference on Computational Intelligence (Bind 1, s. 110-120). SCITEPRESS Digital Library. https://doi.org/10.5220/0006511301100120
Rasmussen, Christoffer Bøgelund ; Nasrollahi, Kamal ; Moeslund, Thomas B. / R-FCN Object Detection Ensemble based on Object Resolution and Image Quality. International Joint Conference on Computational Intelligence. Bind 1 SCITEPRESS Digital Library, 2017. s. 110-120
@inproceedings{1d95304ddbad45038e78759b23dcec56,
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abstract = "Object detection can be difficult due to challenges such as variations in objects both inter- and intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination strategies were explored for combining the individual member detections into an ensemble result, namely average and a weighted average. R-FCNs were trained and tested on the PASCAL VOC benchmark object detection dataset. Results proved positive with an increase in Average Precision (AP), compared to state-of-the-art similar systems, when ensemble members were combined appropriately.",
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Rasmussen, CB, Nasrollahi, K & Moeslund, TB 2017, R-FCN Object Detection Ensemble based on Object Resolution and Image Quality. i International Joint Conference on Computational Intelligence. bind 1, SCITEPRESS Digital Library, s. 110-120, Funchal, Portugal, 01/11/2017. https://doi.org/10.5220/0006511301100120

R-FCN Object Detection Ensemble based on Object Resolution and Image Quality. / Rasmussen, Christoffer Bøgelund; Nasrollahi, Kamal; Moeslund, Thomas B.

International Joint Conference on Computational Intelligence. Bind 1 SCITEPRESS Digital Library, 2017. s. 110-120.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - R-FCN Object Detection Ensemble based on Object Resolution and Image Quality

AU - Rasmussen, Christoffer Bøgelund

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

PY - 2017

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N2 - Object detection can be difficult due to challenges such as variations in objects both inter- and intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination strategies were explored for combining the individual member detections into an ensemble result, namely average and a weighted average. R-FCNs were trained and tested on the PASCAL VOC benchmark object detection dataset. Results proved positive with an increase in Average Precision (AP), compared to state-of-the-art similar systems, when ensemble members were combined appropriately.

AB - Object detection can be difficult due to challenges such as variations in objects both inter- and intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination strategies were explored for combining the individual member detections into an ensemble result, namely average and a weighted average. R-FCNs were trained and tested on the PASCAL VOC benchmark object detection dataset. Results proved positive with an increase in Average Precision (AP), compared to state-of-the-art similar systems, when ensemble members were combined appropriately.

KW - Convolutional Neural Networks

KW - Ensemble Learning

KW - Object Detection

KW - Image Quality Assessment

U2 - 10.5220/0006511301100120

DO - 10.5220/0006511301100120

M3 - Article in proceeding

SN - 978-989-758-274-5

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BT - International Joint Conference on Computational Intelligence

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Rasmussen CB, Nasrollahi K, Moeslund TB. R-FCN Object Detection Ensemble based on Object Resolution and Image Quality. I International Joint Conference on Computational Intelligence. Bind 1. SCITEPRESS Digital Library. 2017. s. 110-120 https://doi.org/10.5220/0006511301100120