Evaluation Of The Neural Network Object Detection In Multi-Modal Images

Ligocki, Adam

Evaluation Of The Neural Network Object Detection In Multi-Modal Images

Files

156_EEICT_2021_2.pdf(3.92 MB)

Date

2021

Authors

Ligocki, Adam

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

This paper studies the information gain of various data domains that are commonly usedin the modern Advanced Driving Assistant Systems (ADAS) to develop robust systems that wouldincrease 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 objectdetection and semantic scene segmentation. We created a small annotated dataset of synchronizedRGB, grayscale, thermal, and depth map images and used the modern DCNN framework tool toevaluate the object detection robustness of different data domains and their information gain processunderstanding the surrounding environment of the semi-autonomous driving agent.

Keywords

Multi-modal, Object Detection, Convolutional Neural Network, RGB, Grayscale, Thermal,IR, Depth Map

Citation

Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 156-160. ISBN 978-80-214-5943-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence