FotonNet: A Hardware-efficient Object Detection System using 3D-depth Segmentation and 2D-deep Neural Network Classifier

Gurjeet Singh, Sunmiao, Shi Shi, Patrick Chiang

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 information 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.

Download


Paper Citation


in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 461-468. DOI: 10.5220/0008958604610468


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2020},
pages={461-468},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008958604610468},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: 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
AU - Singh G.
AU - Sunmiao.
AU - Shi S.
AU - Chiang P.
PY - 2020
SP - 461
EP - 468
DO - 10.5220/0008958604610468