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Detail publikace
LIGOCKI, A.
Originální název
EVALUATIONOFTHENEURALNETWORKOBJECT DETECTIONINMULTI-MODALIMAGES
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper studies the information gain of various data domains that are commonly used in the modern Advanced Driving Assistant Systems (ADAS) to develop robust systems that would increase traffic safety. We could see a fast growth of many Deep Convolutional Neural Networks (DCNN) based solutions during the last several years. These methods are state-of-the-art in object detection and semantic scene segmentation. We created a small annotated dataset of synchronized RGB, grayscale, thermal, and depth map images and used the modern DCNN framework tool to evaluate the object detection robustness of different data domains and their information gain process understanding the surrounding environment of the semi-autonomous driving agent.
Klíčová slova
Multi-modal, Object Detection, Convolutional Neural Network, RGB, Grayscale, Thermal, IR, Depth Map
Autoři
Vydáno
27. 4. 2021
Nakladatel
Brno University of Technology, Faculty of Electrical Engineering and Communication
Místo
Brno
ISBN
978-80-214-5943-4
Kniha
PROCEEDINGS II OF THE 27TH STUDENT EEICT 2021 selected papers
Edice
1
Strany od
156
Strany do
160
Strany počet
5
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf
BibTex
@inproceedings{BUT171475, author="Adam {Ligocki}", title="EVALUATIONOFTHENEURALNETWORKOBJECT DETECTIONINMULTI-MODALIMAGES", booktitle="PROCEEDINGS II OF THE 27TH STUDENT EEICT 2021 selected papers", year="2021", series="1", pages="156--160", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", doi="10.13164/eeict.2021.156", isbn="978-80-214-5943-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf" }