DDR-Net提升航拍图像中小目标检测精度，自适应优化特征与锚框。
A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images
- 基于数据驱动自动选择最优特征图和锚框，无需人工调参。
- 在有限数据下仍保持高精度，实测优于RetinaNet等主流模型。
- 适合遥感、农业、安防等领域中需精准识别小目标的场景。
在航拍影像领域，小目标检测对环境监测、城市规划和应急响应至关重要。本文提出一种基于数据驱动的深度学习模型DDR-Net，旨在提升小目标检测能力。DDR-Net引入新型数据驱动技术，可自主确定最优特征图与锚框估计，实现定制化高效训练过程，同时保持检测精度。此外，提出一种创新采样策略，增强模型在数据受限条件下的性能表现。实验结果表明，该模型在多个航拍鸟类图像数据集上显著优于RetinaNet及其它现有模型。该方法大幅降低数据采集与训练成本，具备高效性与实用性，可广泛应用于野生动物监测、交通流优化与公共安全等领域，推动航拍图像分析技术发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。