Dog face detection using yolo network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526298" target="_blank" >RIV/70883521:28140/20:63526298 - isvavai.cz</a>

Výsledek na webu

<a href="https://mendel-journal.org/index.php/mendel/article/view/121" target="_blank" >https://mendel-journal.org/index.php/mendel/article/view/121</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/mendel.2020.2.017" target="_blank" >10.13164/mendel.2020.2.017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dog face detection using yolo network

Popis výsledku v původním jazyce

This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92. © 2020, Brno University of Technology. All rights reserved.

Název v anglickém jazyce

Dog face detection using yolo network

Popis výsledku anglicky

This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92. © 2020, Brno University of Technology. All rights reserved.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Mendel

ISSN

1803-3814

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

17-22

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85098254118