融合YOLOv11与MobileNetV3-SSD，实现遥感图像高效目标识别与自动标注。
Large-scale Remote Sensing Image Target Recognition and Automatic Annotation
- 用集成学习融合YOLOv11与MobileNetV3-SSD提升检测性能。
- 通过泊松盘采样与EIOU度量优化分割训练与推理过程。
- 兼顾精度与速度，适合大规模遥感图像处理场景。
本文提出一种名为LRSAA的大面积遥感图像目标识别与自动标注方法。该方法通过集成学习融合YOLOv11与MobileNetV3-SSD目标检测算法，提升模型性能。同时，采用泊松盘采样分割技术与EIOU度量优化分割图像的训练与推理流程，并整合结果。该方法在降低计算资源需求的同时，实现了精度与速度的良好平衡。项目源代码已公开于https://github.com/anaerovane/LRSAA。
原文摘要 · Abstract (English)
This paper presents a method for object recognition and automatic labeling in large-area remote sensing images called LRSAA. The method integrates YOLOv11 and MobileNetV3-SSD object detection algorithms through ensemble learning to enhance model performance. Furthermore, it employs Poisson disk sampling segmentation techniques and the EIOU metric to optimize the training and inference processes of segmented images, followed by the integration of results. This approach not only reduces the demand for computational resources but also achieves a good balance between accuracy and speed. The source code for this project has been made publicly available on https://github.com/anaerovane/LRSAA.
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