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Performance Analysis of RIWM and LDVM Router Replacement Methods for WMNs by WMN-PSOSA-DGA Hybrid Simulation System Considering Stadium Distribution of Mesh Clients Read chapter Read first chapter

2022 | OriginalPaper | Chapter

A Transfer Learning-Based Object Detection and Annotation System: Performance Evaluation for Vehicle Objects from Onboard Camera

Authors : Yoshiki Tada, Masahiro Miwata, Shota Uchimura, Makoto Ikeda, Leonard Barolli

Published in: Advances on P2P, Parallel, Grid, Cloud and Internet Computing

Publisher: Springer International Publishing

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Abstract

It is a challenge to collect the required image data rapidly and effectively for object detection in emergency disaster situations. In this work, we focus on improving the classification accuracy and duration by collecting object images parallelly in the target disaster environment. In this paper, we propose a Transfer Learning (TL)-based object detection and annotation system. Our system has a novel image selection function to reduce the similar images for preparing a dataset that can improve the accuracy and model training duration. Our system train the model considering vehicular objects and their corresponding labels from video data of onboard camera. From the evaluation results, we observed that our proposed image selection function for training can reduce the number of sequential similar images to about 23%. Also, our TL-based object detection system can improve the detection performance compared with conventional learning method.

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previous chapter Performance Analysis of RIWM and LDVM Router Replacement Methods for WMNs by WMN-PSOSA-DGA Hybrid Simulation System Considering Stadium Distribution of Mesh Clients
next chapter Fuzzy Randomized Load Balancing for Cloud Computing
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Metadata
Title
A Transfer Learning-Based Object Detection and Annotation System: Performance Evaluation for Vehicle Objects from Onboard Camera
Authors
Yoshiki Tada
Masahiro Miwata
Shota Uchimura
Makoto Ikeda
Leonard Barolli
Copyright Year
2022
Book
Advances on P2P, Parallel, Grid, Cloud and Internet Computing

Print ISBN: 978-3-030-89898-4

Electronic ISBN: 978-3-030-89899-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-89899-1_2