Resumen
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.