一种用于航空图像小目标检测的数据驱动RetinaNet模型
A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images
AI总结：
本研究针对航空图像小目标检测需求，提出数据驱动的DDR-Net模型，可自主确定最优特征图与锚框，搭配创新采样技术提升有限数据下的性能，实验证明其效果优于RetinaNet等现有模型，可支撑多领域应用。
摘要：
在航空成像领域，小目标检测能力对环境监测、城市规划、危机管理等众多应用至关重要。本研究基于RetinaNet提出了DDR-Net：一种数据驱动的深度学习模型，旨在提升微小目标的检测效果。DDR-Net引入了新颖的数据驱动技术，可自主确定最优特征图与锚框估计，在保持精度的同时实现定制化、高效的训练流程。此外，本文提出了一种创新的采样技术，以提升数据有限训练约束下的模型性能。该模型增强的检测能力可通过精准识别车辆、行人等小目标，支撑野生动物与栖息地监测、交通流优化、公共安全提升等关键应用。DDR-Net大幅降低了数据收集与训练所需的成本和时间，即便在数据有限的情况下也能实现高效性能。在各类航空鸟类图像数据集上的实证评估表明，DDR-Net的性能显著优于RetinaNet及其他现有模型。这些创新推动了当前航空图像分析技术的发展，有望在农业、安防、考古等多个领域产生广泛影响。
英文摘要：
In the realm of aerial imaging, the ability to detect small objects is pivotal for a myriad of applications, encompassing environmental surveillance, urban design, and crisis management. Leveraging RetinaNet, this work unveils DDR-Net: a data-driven, deep-learning model devised to enhance the detection of diminutive objects. DDR-Net introduces novel, data-driven techniques to autonomously ascertain optimal feature maps and anchor estimations, cultivating a tailored and proficient training process while maintaining precision. Additionally, this paper presents an innovative sampling technique to bolster model efficacy under limited data training constraints. The model's enhanced detection capabilities support critical applications including wildlife and habitat monitoring, traffic flow optimization, and public safety improvements through accurate identification of small objects like vehicles and pedestrians. DDR-Net significantly reduces the cost and time required for data collection and training, offering efficient performance even with limited data. Empirical assessments over assorted aerial avian imagery datasets demonstrate that DDR-Net markedly surpasses RetinaNet and alternative contemporary models. These innovations advance current aerial image analysis technologies and promise wide-ranging impacts across multiple sectors including agriculture, security, and archaeology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
