Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions
The reliable and accurate detection of bridges plays an important role in imaging-driven transportation surveillance. It is capable of timely providing the traffic information, leading to safer and more convenient transportation. However, the visual quality of observed images is often inevitably red...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6932040 |
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| author | Peng Han Xiaoxia Yang |
| author_facet | Peng Han Xiaoxia Yang |
| author_sort | Peng Han |
| collection | DOAJ |
| description | The reliable and accurate detection of bridges plays an important role in imaging-driven transportation surveillance. It is capable of timely providing the traffic information, leading to safer and more convenient transportation. However, the visual quality of observed images is often inevitably reduced owing to the adverse weather conditions, e.g., haze and low lightness. It is still difficult to adopt the existing powerful deep learning methods to reliably and accurately detect the bridges under different imaging conditions. To achieve satisfactory bridge detection results, we first propose to exploit the data augmentation strategy and physical imaging method to generate the natural-looking experimental dataset, which contains latent high-quality images and their hazy and low-light versions. We then investigate how to further promote the deep learning-based bridge detection methods through the manually generated dataset. It is obvious that the generalization abilities of these deep neural networks are significantly improved using this data augmentation strategy. In this work, we constructed an original dataset consisting of 3500 images of size 900×600, collected under normal imaging condition. Extensive detection experiments will be performed based on the augmented dataset. Experimental results have demonstrated that our automatic bridge detection framework could generate more reliable and accurate results compared with existing detection methods. |
| format | Article |
| id | doaj-art-060b4ef4f44743ffb706b72b0cd9a0b6 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-060b4ef4f44743ffb706b72b0cd9a0b62025-02-03T01:22:49ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6932040Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging ConditionsPeng Han0Xiaoxia Yang1The Administrative Center for China’s Agenda 21 (ACCA 21)College of Earth ScienceThe reliable and accurate detection of bridges plays an important role in imaging-driven transportation surveillance. It is capable of timely providing the traffic information, leading to safer and more convenient transportation. However, the visual quality of observed images is often inevitably reduced owing to the adverse weather conditions, e.g., haze and low lightness. It is still difficult to adopt the existing powerful deep learning methods to reliably and accurately detect the bridges under different imaging conditions. To achieve satisfactory bridge detection results, we first propose to exploit the data augmentation strategy and physical imaging method to generate the natural-looking experimental dataset, which contains latent high-quality images and their hazy and low-light versions. We then investigate how to further promote the deep learning-based bridge detection methods through the manually generated dataset. It is obvious that the generalization abilities of these deep neural networks are significantly improved using this data augmentation strategy. In this work, we constructed an original dataset consisting of 3500 images of size 900×600, collected under normal imaging condition. Extensive detection experiments will be performed based on the augmented dataset. Experimental results have demonstrated that our automatic bridge detection framework could generate more reliable and accurate results compared with existing detection methods.http://dx.doi.org/10.1155/2022/6932040 |
| spellingShingle | Peng Han Xiaoxia Yang Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions Journal of Advanced Transportation |
| title | Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions |
| title_full | Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions |
| title_fullStr | Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions |
| title_full_unstemmed | Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions |
| title_short | Deep Learning-Enabled Automatic Detection of Bridges for Promoting Transportation Surveillance under Different Imaging Conditions |
| title_sort | deep learning enabled automatic detection of bridges for promoting transportation surveillance under different imaging conditions |
| url | http://dx.doi.org/10.1155/2022/6932040 |
| work_keys_str_mv | AT penghan deeplearningenabledautomaticdetectionofbridgesforpromotingtransportationsurveillanceunderdifferentimagingconditions AT xiaoxiayang deeplearningenabledautomaticdetectionofbridgesforpromotingtransportationsurveillanceunderdifferentimagingconditions |