| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0644268 |
| utime |
20260115101637.4 |
| mtime |
20260108235959.9 |
| DOI |
10.1007/978-3-032-10192-1_14 |
| title
(primary) (eng) |
WARD: Weather-Aware Road Surface Condition Monitoring Dataset |
| specification |
| page_count |
12 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0644267 |
| ISBN |
978-3-032-10192-1 |
| title
|
Image Analysis and Processing - ICIAP 2025 |
| part_title |
Part II |
| page_num |
164-175 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2026 |
|
| editor |
| name1 |
Galasso |
| name2 |
Fabio |
|
| editor |
|
|
| keyword |
computer vision |
| keyword |
environmental monitoring |
| keyword |
object detection |
| author
(primary) |
| ARLID |
cav_un_auth*0500746 |
| name1 |
Nesnídalová |
| name2 |
Soňa |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept (eng) |
Department of Image Processing |
| department (cz) |
ZOI |
| department (eng) |
ZOI |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0379363 |
| name1 |
Kerepecký |
| name2 |
Tomáš |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0283562 |
| name1 |
Novozámský |
| name2 |
Adam |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-10069S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0472834 |
|
| abstract
(eng) |
Road surface condition (RSC) monitoring is essential for enhancing vehicle safety and accident prevention. This study investigates the application of computer vision techniques for real-time sensing of road surface conditions. We introduce a novel dataset named WARD (Weather-Aware Road Dataset), a comprehensive collection of almost 55 000 images collected in real-world driving scenarios across diverse seasonal and weather conditions, designed to advance RSC detection, now available for download. We thoroughly evaluate state-of-the-art computer vision models, specifically MobileNet and EfficientNet, on both the WARD and publicly available RoadSaW datasets, providing insights into their classification performance. MobileNet exhibited superior classification and inference speed results, processing images at up to 30 fps on an affordable GPU. To improve real-time efficiency, we employ temporal smoothing through moving window aggregation. Our findings validate the potential of non-contact, camera-based RSC monitoring, showcasing its practicality and cost-effectiveness compared to other sensors. |
| action |
| ARLID |
cav_un_auth*0500747 |
| name |
International Conference on Image Analysis and Processing – ICIAP 2025 /23./ |
| dates |
20250915 |
| mrcbC20-s |
20250919 |
| place |
Roma |
| country |
IT |
|
| RIV |
JC |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2026 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0374433 |
| cooperation |
| ARLID |
cav_un_auth*0377559 |
| name |
ČVUT v Praze, Fakulta jaderného a fyzikálního inženýrství |
| institution |
ČVUT FJFI |
| country |
CZ |
|
| confidential |
S |
| arlyear |
2026 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0644267 Image Analysis and Processing - ICIAP 2025 Part II 978-3-032-10192-1 164 175 Cham Springer 2026 Lecture Notes in Computer Science 16168 |
| mrcbU67 |
Rodolà Emanuele 340 |
| mrcbU67 |
Galasso Fabio 340 |
| mrcbU67 |
Masi Iacopo 340 |
|