Road pavement crack detection using deep learning with synthetic data; IOP Conference Series: Materials Science and Engineering; Vol. 1019 : 14th International Forum on Strategic Technology (IFOST 2019)
| Parent link: | IOP Conference Series: Materials Science and Engineering Vol. 1019 : 14th International Forum on Strategic Technology (IFOST 2019).— 2021.— [012036, 10 p.] |
|---|---|
| Main Author: | |
| Corporate Author: | |
| Other Authors: | |
| Summary: | Title screen The improvement of road system quality is a critical task. The mechanism to address such important issue is close monitoring of road pavement condition. Traditional approach requires manual identification of damages. Taking into account considerable length of road system it is essential to create an effective automatic pavement defects detection tool. This approach will extremely reduce time for monitoring of current road state. In this paper global experience in solution of detection issues of road pavement's distress is reviewed. The article includes information about the existing datasets of road defects, which are commonly used for detection and segmentation. The present work is based on deep learning approach with the use of synthetic generated training data for segmentation of cracks in driver-view image. The novelty of the approach lies in creating synthetic dataset for training state-of-the-art deep learning frameworks. The relevance of the research is emphasized by processing of wide-view images in which heterogeneous pixel intensity, complex crack topology, different illumination condition and complexity of background make the task challenging. |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | http://earchive.tpu.ru/handle/11683/64552 https://doi.org/10.1088/1757-899X/1019/1/012036 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663542 |
MARC
| LEADER | 00000naa2a2200000 4500 | ||
|---|---|---|---|
| 001 | 663542 | ||
| 005 | 20251216135207.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\34712 | ||
| 035 | |a RU\TPU\network\34710 | ||
| 090 | |a 663542 | ||
| 100 | |a 20210218d2021 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a Road pavement crack detection using deep learning with synthetic data |f I. A. Kanaeva, Yu. A. Ivanova | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 21 tit.] | ||
| 330 | |a The improvement of road system quality is a critical task. The mechanism to address such important issue is close monitoring of road pavement condition. Traditional approach requires manual identification of damages. Taking into account considerable length of road system it is essential to create an effective automatic pavement defects detection tool. This approach will extremely reduce time for monitoring of current road state. In this paper global experience in solution of detection issues of road pavement's distress is reviewed. The article includes information about the existing datasets of road defects, which are commonly used for detection and segmentation. The present work is based on deep learning approach with the use of synthetic generated training data for segmentation of cracks in driver-view image. The novelty of the approach lies in creating synthetic dataset for training state-of-the-art deep learning frameworks. The relevance of the research is emphasized by processing of wide-view images in which heterogeneous pixel intensity, complex crack topology, different illumination condition and complexity of background make the task challenging. | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\network\2008 |t IOP Conference Series: Materials Science and Engineering | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\34482 |t Vol. 1019 : 14th International Forum on Strategic Technology (IFOST 2019) |o October 14-17, 2019, Tomsk, Russia |f National Research Tomsk Polytechnic University (TPU) ; eds. R. V. Ostvald, D. Gvozdeva, A. S. Ryabikina |v [012036, 10 p.] |d 2021 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a трещины | |
| 610 | 1 | |a дорожные покрытия | |
| 610 | 1 | |a дефекты | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a изображения | |
| 700 | 1 | |a Kanaeva |b I. A. | |
| 701 | 1 | |a Ivanova |b Yu. A. |c Specialist in the field of informatics and computer technology |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1986- |g Yulia Aleksandrovna |3 (RuTPU)RU\TPU\pers\45859 |9 22004 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
| 801 | 2 | |a RU |b 63413507 |c 20210301 |g RCR | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/64552 | |
| 856 | 4 | |u https://doi.org/10.1088/1757-899X/1019/1/012036 | |
| 942 | |c CF | ||