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Authors: Gurjeet Singh 1 ;  Sunmiao 2 ; Shi Shi 2 and Patrick Chiang 1 ; 3 ; 2

Affiliations: 1 Dept. of EECS, Oregon State University, Corvallis, U.S.A. ; 2 State Key Laboratory of ASIC & System, Fudan University, Shanghai, China ; 3 PhotonIC Technologies, Shanghai, China

Keyword(s): Object Detection, 3D Data, Hardware, Depth Sensors.

Abstract: Object detection and classification is one of the most crucial computer vision problems. Ever since the introduction of deep learning, we have witnessed a dramatic increase in the accuracy of this object detection problem. However, most of these improvements have occurred using conventional 2D image processing. Recently, low-cost 3D-image sensors, such as the Microsoft Kinect (Time-of-Flight) or the Apple FaceID (Structured-Light), can provide 3D-depth or point cloud data that can be added to a convolutional neural network, acting as an extra set of dimensions. We are proposing a hardware-based approach for Object Detection by moving region of interest identification closer to sensor node in the hardware. Due to this approach, we do not need a large dataset with depth images to retrain the network. Our 2D + 3D system takes the 3D-data to determine the object region followed by any conventional 2D-DNN, such as AlexNet. In this method, our approach can readily dissociate the informa tion collected from the Point Cloud and 2D-Image data and combine both operations later. Hence, our system can use any existing trained 2D network on a large image dataset and does not require a large 3D-depth dataset for new training. Experimental object detection results across 30 images show an accuracy of 0.67, whereas 0.54 and 0.51 for FasterRCNN and YOLO, respectively. (More)

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Paper citation in several formats:
Singh, G. ; Sunmiao. ; Shi, S. and Chiang, P. (2020). FotonNet: A Hardware-efficient Object Detection System using 3D-depth Segmentation and 2D-deep Neural Network Classifier. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-397-1; ISSN 2184-4313, SciTePress, pages 461-468. DOI: 10.5220/0008958604610468

@conference{icpram20,
author={Gurjeet Singh and Sunmiao and Shi Shi and Patrick Chiang},
title={FotonNet: A Hardware-efficient Object Detection System using 3D-depth Segmentation and 2D-deep Neural Network Classifier},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2020},
pages={461-468},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008958604610468},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - FotonNet: A Hardware-efficient Object Detection System using 3D-depth Segmentation and 2D-deep Neural Network Classifier
SN - 978-989-758-397-1
IS - 2184-4313
AU - Singh, G.
AU - Sunmiao.
AU - Shi, S.
AU - Chiang, P.
PY - 2020
SP - 461
EP - 468
DO - 10.5220/0008958604610468
PB - SciTePress

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