Monitoring and Identification of Road Construction Safety Factors via UAV
The safety of road construction is one of the most important concerns of construction managers for the following reasons: long-span construction operation, no fixed monitoring cameras, and huge impacts on existing traffic, while the managers still rely on manual inspection and a lack of image record...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/22/8797 |
| _version_ | 1827643634098372608 |
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| author | Chendong Zhu Junqing Zhu Tianxiang Bu Xiaofei Gao |
| author_facet | Chendong Zhu Junqing Zhu Tianxiang Bu Xiaofei Gao |
| author_sort | Chendong Zhu |
| collection | DOAJ |
| description | The safety of road construction is one of the most important concerns of construction managers for the following reasons: long-span construction operation, no fixed monitoring cameras, and huge impacts on existing traffic, while the managers still rely on manual inspection and a lack of image records. With the fast development of Unmanned Aerial Vehicle (UAV) and Artificial Intelligence (AI), monitoring safety concerns of road construction sites becomes easily accessible. This research aims to integrate UAVs and AI to establish a UAV-based road construction safety monitoring platform. In this study, road construction safety factors including constructors, construction vehicles, safety signs, and guardrails are defined and monitored to make up for the lack of image data at the road construction site. The main findings of this study include three aspects. First, the flight and photography schemes are proposed based on the UAV platform for information collection for road construction. Second, deep learning algorithms including YOLOv4 and DeepSORT are utilized to automatically detect and track safety factors. Third, a road construction dataset is established with 3594 images. The results show that the UAV-based monitoring platform can help managers with security inspection and recording images. |
| first_indexed | 2024-03-09T18:00:49Z |
| format | Article |
| id | doaj.art-3869caeddca749e19e4d49530072fa9c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:00:49Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3869caeddca749e19e4d49530072fa9c2023-11-24T09:55:53ZengMDPI AGSensors1424-82202022-11-012222879710.3390/s22228797Monitoring and Identification of Road Construction Safety Factors via UAVChendong Zhu0Junqing Zhu1Tianxiang Bu2Xiaofei Gao3School of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaThe safety of road construction is one of the most important concerns of construction managers for the following reasons: long-span construction operation, no fixed monitoring cameras, and huge impacts on existing traffic, while the managers still rely on manual inspection and a lack of image records. With the fast development of Unmanned Aerial Vehicle (UAV) and Artificial Intelligence (AI), monitoring safety concerns of road construction sites becomes easily accessible. This research aims to integrate UAVs and AI to establish a UAV-based road construction safety monitoring platform. In this study, road construction safety factors including constructors, construction vehicles, safety signs, and guardrails are defined and monitored to make up for the lack of image data at the road construction site. The main findings of this study include three aspects. First, the flight and photography schemes are proposed based on the UAV platform for information collection for road construction. Second, deep learning algorithms including YOLOv4 and DeepSORT are utilized to automatically detect and track safety factors. Third, a road construction dataset is established with 3594 images. The results show that the UAV-based monitoring platform can help managers with security inspection and recording images.https://www.mdpi.com/1424-8220/22/22/8797road constructionsafetyunmannedaerialvehicleconvolutional neural network |
| spellingShingle | Chendong Zhu Junqing Zhu Tianxiang Bu Xiaofei Gao Monitoring and Identification of Road Construction Safety Factors via UAV Sensors road construction safety unmanned aerial vehicle convolutional neural network |
| title | Monitoring and Identification of Road Construction Safety Factors via UAV |
| title_full | Monitoring and Identification of Road Construction Safety Factors via UAV |
| title_fullStr | Monitoring and Identification of Road Construction Safety Factors via UAV |
| title_full_unstemmed | Monitoring and Identification of Road Construction Safety Factors via UAV |
| title_short | Monitoring and Identification of Road Construction Safety Factors via UAV |
| title_sort | monitoring and identification of road construction safety factors via uav |
| topic | road construction safety unmanned aerial vehicle convolutional neural network |
| url | https://www.mdpi.com/1424-8220/22/22/8797 |
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