Towards dependable object detection
A high confidence in object detection is very crucial for object detector modules to be used in real world applications. Though the confidence scores in object detection can be improved by using better and larger training data set and using more robust architectures, which is an approach from the co...
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sg-ntu-dr.10356-1534472021-12-03T08:06:14Z Towards dependable object detection Muthuchamy Selvaraj, Nithish Muhammad, Ilyas Cheah, Chien Chern School of Electrical and Electronic Engineering 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020) Centre for system intelligence and efficiency (EXQUISITUS) Engineering::Electrical and electronic engineering Object Detector Viewing Point Visual Servoing Mobile Robot A high confidence in object detection is very crucial for object detector modules to be used in real world applications. Though the confidence scores in object detection can be improved by using better and larger training data set and using more robust architectures, which is an approach from the computer vision side, this paper aims to improve the detection confidence by finding the effective viewpoints which makes a dependable use of the available object detectors. In particular, we investigate the effect of viewing distance on detection confidence of an object detector, which can further be used to control a camera mounted on a mobile robot to approach a better viewing position. We consider the cases with both fixed focal length and variable focal length camera. Experimental results are presented to demonstrate the efficacy and validity of the techniques presented in this work. Agency for Science, Technology and Research (A*STAR) This work was supported by the Agency for Science, Technology and Research of Singapore (A*STAR), under the National Robotics Programme (NRP) - Robotics Domain Specific (RDS) (Ref # 1922200001). 2021-12-03T08:05:23Z 2021-12-03T08:05:23Z 2020 Conference Paper Muthuchamy Selvaraj, N., Muhammad, I. & Cheah, C. C. (2020). Towards dependable object detection. 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020), 523-528. https://dx.doi.org/10.1109/IECON43393.2020.9255076 9781728154145 2577-1647 https://hdl.handle.net/10356/153447 10.1109/IECON43393.2020.9255076 2-s2.0-85097737076 523 528 en NRP-RDS-Ref # 1922200001 © 2020 IEEE. All rights reserved. |
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Engineering::Electrical and electronic engineering Object Detector Viewing Point Visual Servoing Mobile Robot Muthuchamy Selvaraj, Nithish Muhammad, Ilyas Cheah, Chien Chern Towards dependable object detection |
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A high confidence in object detection is very crucial for object detector modules to be used in real world applications. Though the confidence scores in object detection can be improved by using better and larger training data set and using more robust architectures, which is an approach from the computer vision side, this paper aims to improve the detection confidence by finding the effective viewpoints which makes a dependable use of the available object detectors. In particular, we investigate the effect of viewing distance on detection confidence of an object detector, which can further be used to control a camera mounted on a mobile robot to approach a better viewing position. We consider the cases with both fixed focal length and variable focal length camera. Experimental results are presented to demonstrate the efficacy and validity of the techniques presented in this work. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Muthuchamy Selvaraj, Nithish Muhammad, Ilyas Cheah, Chien Chern |
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Conference or Workshop Item |
author |
Muthuchamy Selvaraj, Nithish Muhammad, Ilyas Cheah, Chien Chern |
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Muthuchamy Selvaraj, Nithish |
title |
Towards dependable object detection |
title_short |
Towards dependable object detection |
title_full |
Towards dependable object detection |
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Towards dependable object detection |
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Towards dependable object detection |
title_sort |
towards dependable object detection |
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2021 |
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https://hdl.handle.net/10356/153447 |
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1718368092874080256 |