Cargando…
Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection
Anomalous road manhole covers pose a potential risk to road safety in cities. In the development of smart cities, computer vision techniques use deep learning to automatically detect anomalous manhole covers to avoid these risks. One important problem is that a large amount of data are required to t...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007452/ https://www.ncbi.nlm.nih.gov/pubmed/36904880 http://dx.doi.org/10.3390/s23052676 |
_version_ | 1784905525038678016 |
---|---|
author | Zhang, Dongping Yu, Xuecheng Yang, Li Quan, Daying Mi, Hongmei Yan, Ke |
author_facet | Zhang, Dongping Yu, Xuecheng Yang, Li Quan, Daying Mi, Hongmei Yan, Ke |
author_sort | Zhang, Dongping |
collection | PubMed |
description | Anomalous road manhole covers pose a potential risk to road safety in cities. In the development of smart cities, computer vision techniques use deep learning to automatically detect anomalous manhole covers to avoid these risks. One important problem is that a large amount of data are required to train a road anomaly manhole cover detection model. The number of anomalous manhole covers is usually small, which makes it a challenge to create training datasets quickly. To expand the dataset and improve the generalization of the model, researchers usually copy and paste samples from the original data to other data in order to achieve data augmentation. In this paper, we propose a new data augmentation method, which uses data that do not exist in the original dataset as samples to automatically select the pasting position of manhole cover samples and predict the transformation parameters via visual prior experience and perspective transformations, making it more accurately capture the actual shape of manhole covers on a road. Without using other data enhancement processes, our method raises the mean average precision (mAP) by at least 6.8 compared with the baseline model. |
format | Online Article Text |
id | pubmed-10007452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100074522023-03-12 Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection Zhang, Dongping Yu, Xuecheng Yang, Li Quan, Daying Mi, Hongmei Yan, Ke Sensors (Basel) Article Anomalous road manhole covers pose a potential risk to road safety in cities. In the development of smart cities, computer vision techniques use deep learning to automatically detect anomalous manhole covers to avoid these risks. One important problem is that a large amount of data are required to train a road anomaly manhole cover detection model. The number of anomalous manhole covers is usually small, which makes it a challenge to create training datasets quickly. To expand the dataset and improve the generalization of the model, researchers usually copy and paste samples from the original data to other data in order to achieve data augmentation. In this paper, we propose a new data augmentation method, which uses data that do not exist in the original dataset as samples to automatically select the pasting position of manhole cover samples and predict the transformation parameters via visual prior experience and perspective transformations, making it more accurately capture the actual shape of manhole covers on a road. Without using other data enhancement processes, our method raises the mean average precision (mAP) by at least 6.8 compared with the baseline model. MDPI 2023-03-01 /pmc/articles/PMC10007452/ /pubmed/36904880 http://dx.doi.org/10.3390/s23052676 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Dongping Yu, Xuecheng Yang, Li Quan, Daying Mi, Hongmei Yan, Ke Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection |
title | Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection |
title_full | Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection |
title_fullStr | Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection |
title_full_unstemmed | Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection |
title_short | Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection |
title_sort | data-augmented deep learning models for abnormal road manhole cover detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007452/ https://www.ncbi.nlm.nih.gov/pubmed/36904880 http://dx.doi.org/10.3390/s23052676 |
work_keys_str_mv | AT zhangdongping dataaugmenteddeeplearningmodelsforabnormalroadmanholecoverdetection AT yuxuecheng dataaugmenteddeeplearningmodelsforabnormalroadmanholecoverdetection AT yangli dataaugmenteddeeplearningmodelsforabnormalroadmanholecoverdetection AT quandaying dataaugmenteddeeplearningmodelsforabnormalroadmanholecoverdetection AT mihongmei dataaugmenteddeeplearningmodelsforabnormalroadmanholecoverdetection AT yanke dataaugmenteddeeplearningmodelsforabnormalroadmanholecoverdetection |