推进ESG和可持续发展目标11:加强基于yolov7的无人机探测,促进城市和社区的可持续交通

IF 2.1 Q3 ENVIRONMENTAL SCIENCES Urban science (Basel, Switzerland) Pub Date : 2023-10-17 DOI:10.3390/urbansci7040108
Ming-An Chung, Tze-Hsun Wang, Chia-Wei Lin
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引用次数: 0

摘要

环境、社会和治理问题最近得到了显著的突出,特别是随着对环境保护的日益重视。在环境问题日益严重的情况下,无人机已经成为解决可持续交通挑战的关键资产。本研究的重点是在可持续交通领域中提高无人机的目标检测能力。该方法对YOLOv7 E-ELAN模型进行了细化,明确针对交通场景进行了定制。利用深度学习和计算机视觉方面的进步,调整后的模型在平均精度上有所提高,在VisDrone2019数据集上优于原始模型。该方法包括模型组件增强和改进的损失函数,为精确的无人机目标检测建立了有效的策略。这一努力与环境、社会和治理原则无缝结合。此外,它还通过营造安全的城市空间,为实现第11项可持续发展目标作出贡献。随着无人驾驶飞行器成为公共安全和监视不可或缺的一部分,增强检测算法可以为居民创造更安全的环境。可持续交通包括遏制交通拥堵和优化交通系统,其中基于无人机的检测在管理交通流量方面发挥着关键作用,从而支持可持续发展目标11的扩展目标。在公共交通中有效利用无人机有助于减少碳足迹,符合环境、社会和治理原则的“环境可持续性”方面。
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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 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.
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11 weeks
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