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A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs
Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future g...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146989/ https://www.ncbi.nlm.nih.gov/pubmed/37112188 http://dx.doi.org/10.3390/s23083847 |
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author | Jiang, Xian Cui, Qing Wang, Chongguo Wang, Fan Zhao, Yingxiang Hou, Yongjie Zhuang, Rujun Mei, Yunfei Shi, Gang |
author_facet | Jiang, Xian Cui, Qing Wang, Chongguo Wang, Fan Zhao, Yingxiang Hou, Yongjie Zhuang, Rujun Mei, Yunfei Shi, Gang |
author_sort | Jiang, Xian |
collection | PubMed |
description | Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall. |
format | Online Article Text |
id | pubmed-10146989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101469892023-04-29 A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs Jiang, Xian Cui, Qing Wang, Chongguo Wang, Fan Zhao, Yingxiang Hou, Yongjie Zhuang, Rujun Mei, Yunfei Shi, Gang Sensors (Basel) Article Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall. MDPI 2023-04-10 /pmc/articles/PMC10146989/ /pubmed/37112188 http://dx.doi.org/10.3390/s23083847 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 Jiang, Xian Cui, Qing Wang, Chongguo Wang, Fan Zhao, Yingxiang Hou, Yongjie Zhuang, Rujun Mei, Yunfei Shi, Gang A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs |
title | A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs |
title_full | A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs |
title_fullStr | A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs |
title_full_unstemmed | A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs |
title_short | A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs |
title_sort | model for infrastructure detection along highways based on remote sensing images from uavs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146989/ https://www.ncbi.nlm.nih.gov/pubmed/37112188 http://dx.doi.org/10.3390/s23083847 |
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