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

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications

Authors: Christoffer Bøgelund Rasmussen ; Kamal Nasrollahi and Thomas B. Moeslund

Affiliation: Aalborg University, Denmark

ISBN: 978-989-758-274-5

Keyword(s): Convolutional Neural Networks, Object Detection, Image Quality Assessment, Ensemble Learning.

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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Paper citation in several formats:
Rasmussen, C.; Nasrollahi, K. and Moeslund, T. (2017). R-FCN Object Detection Ensemble based on Object Resolution and Image Quality.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 110-120. DOI: 10.5220/0006511301100120

@conference{ijcci17,
author={Christoffer Bøgelund Rasmussen. and Kamal Nasrollahi. and Thomas B. Moeslund.},
title={R-FCN Object Detection Ensemble based on Object Resolution and Image Quality},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={110-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006511301100120},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - R-FCN Object Detection Ensemble based on Object Resolution and Image Quality
SN - 978-989-758-274-5
AU - Rasmussen, C.
AU - Nasrollahi, K.
AU - Moeslund, T.
PY - 2017
SP - 110
EP - 120
DO - 10.5220/0006511301100120

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