Dist-YOLO: fast object detection with distance estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F22%3AA2302C9E" target="_blank" >RIV/61988987:17610/22:A2302C9E - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2076-3417/12/3/1354/html" target="_blank" >https://www.mdpi.com/2076-3417/12/3/1354/html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app12031354" target="_blank" >10.3390/app12031354</a>
Alternative languages
Result language
angličtina
Original language name
Dist-YOLO: fast object detection with distance estimation
Original language description
We present a scheme of how YOLO can be improved in order to predict the absolute distance of objects using only information from a monocular camera. It is fully integrated into the original architecture by extending the prediction vectors, sharing the backbone’s weights with the bounding box regressor, and updating the original loss function by a part responsible for distance estimation. We designed two ways of handling the distance, class-agnostic and class-aware, proving class-agnostic creates smaller prediction vectors than class-aware and achieves better results. We demonstrate that the subtasks of object detection and distance measurement are in synergy, resulting in the increase of the precision of the original bounding box functionality. We show that using the KITTI dataset, the proposed scheme yields a mean relative error of 11% considering all eight classes and the distance range within [0, 150] m, which makes the solution highly competitive with existing approaches. Finally, we show that the inference speed is identical to the unmodified YOLO, 45 frames per second.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Sciences
ISSN
2076-3417
e-ISSN
—
Volume of the periodical
—
Issue of the periodical within the volume
3
Country of publishing house
CH - SWITZERLAND
Number of pages
14
Pages from-to
1-13
UT code for WoS article
000754896100001
EID of the result in the Scopus database
2-s2.0-85123507680