Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities
Environmental, social, and governance issues have gained significant prominence recently, particularly with a growing emphasis on environmental protection. In the realm of heightened environmental concerns, unmanned aerial vehicles have emerged as pivotal assets in addressing transportation challeng...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Urban Science |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2413-8851/7/4/108 |
| _version_ | 1797379224925896704 |
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| author | Ming-An Chung Tze-Hsun Wang Chia-Wei Lin |
| author_facet | Ming-An Chung Tze-Hsun Wang Chia-Wei Lin |
| author_sort | Ming-An Chung |
| collection | DOAJ |
| description | Environmental, social, and governance issues have gained significant prominence recently, particularly with a growing emphasis on environmental protection. In the realm of heightened environmental concerns, unmanned aerial vehicles have emerged as pivotal assets in addressing transportation challenges with a sustainable perspective. This study focuses on enhancing unmanned aerial vehicles’ object detection proficiency within the realm of sustainable transportation. The proposed method refines the YOLOv7 E-ELAN model, tailored explicitly for traffic scenarios. Leveraging strides in deep learning and computer vision, the adapted model demonstrates enhancements in mean average precision, outperforming the original on the VisDrone2019 dataset. This approach, encompassing model component enhancements and refined loss functions, establishes an efficacious strategy for precise unmanned aerial vehicles object detection. This endeavor aligns seamlessly with environmental, social, and governance principles. Moreover, it contributes to the 11th Sustainable Development Goal by fostering secure urban spaces. As unmanned aerial vehicles have become integral to public safety and surveillance, enhancing detection algorithms cultivates safer environments for residents. Sustainable transport encompasses curbing traffic congestion and optimizing transportation systems, where unmanned aerial vehicle-based detection plays a pivotal role in managing traffic flow, thereby supporting extended Sustainable Development Goal 11 objectives. The efficient utilization of unmanned aerial vehicles in public transit significantly aids in reducing carbon footprints, corresponding to the “Environmental Sustainability” facet of Environmental, Social, and Governance principles. |
| first_indexed | 2024-03-08T20:19:04Z |
| format | Article |
| id | doaj.art-5dcf0a8608f343a3aecc0272c659e923 |
| institution | Directory Open Access Journal |
| issn | 2413-8851 |
| language | English |
| last_indexed | 2024-03-08T20:19:04Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj.art-5dcf0a8608f343a3aecc0272c659e9232023-12-22T14:46:42ZengMDPI AGUrban Science2413-88512023-10-017410810.3390/urbansci7040108Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and CommunitiesMing-An Chung0Tze-Hsun Wang1Chia-Wei Lin2Department of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, TaiwanEnvironmental, social, and governance issues have gained significant prominence recently, particularly with a growing emphasis on environmental protection. In the realm of heightened environmental concerns, unmanned aerial vehicles have emerged as pivotal assets in addressing transportation challenges with a sustainable perspective. This study focuses on enhancing unmanned aerial vehicles’ object detection proficiency within the realm of sustainable transportation. The proposed method refines the YOLOv7 E-ELAN model, tailored explicitly for traffic scenarios. Leveraging strides in deep learning and computer vision, the adapted model demonstrates enhancements in mean average precision, outperforming the original on the VisDrone2019 dataset. This approach, encompassing model component enhancements and refined loss functions, establishes an efficacious strategy for precise unmanned aerial vehicles object detection. This endeavor aligns seamlessly with environmental, social, and governance principles. Moreover, it contributes to the 11th Sustainable Development Goal by fostering secure urban spaces. As unmanned aerial vehicles have become integral to public safety and surveillance, enhancing detection algorithms cultivates safer environments for residents. Sustainable transport encompasses curbing traffic congestion and optimizing transportation systems, where unmanned aerial vehicle-based detection plays a pivotal role in managing traffic flow, thereby supporting extended Sustainable Development Goal 11 objectives. The efficient utilization of unmanned aerial vehicles in public transit significantly aids in reducing carbon footprints, corresponding to the “Environmental Sustainability” facet of Environmental, Social, and Governance principles.https://www.mdpi.com/2413-8851/7/4/108sustainable transportenvironmentalsocial and governance (ESG)sustainable development goals (SDGs)unmanned aerial vehicles (UAV)object detection |
| spellingShingle | Ming-An Chung Tze-Hsun Wang Chia-Wei Lin Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities Urban Science sustainable transport environmental social and governance (ESG) sustainable development goals (SDGs) unmanned aerial vehicles (UAV) object detection |
| title | Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities |
| title_full | Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities |
| title_fullStr | Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities |
| title_full_unstemmed | Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities |
| title_short | Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities |
| title_sort | advancing esg and sdgs goal 11 enhanced yolov7 based uav detection for sustainable transportation in cities and communities |
| topic | sustainable transport environmental social and governance (ESG) sustainable development goals (SDGs) unmanned aerial vehicles (UAV) object detection |
| url | https://www.mdpi.com/2413-8851/7/4/108 |
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