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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140805" target="_blank" >RIV/00216305:26220/21:PU140805 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Multi-modal, Object Detection, Convolutional Neural Network, RGB, Grayscale, Thermal, IR, Depth Map
Original language description
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.
Czech name
—
Czech description
—

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)