LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic Annotation
This addresses the problem of efficient target recognition in remote sensing for applications like environmental monitoring, but it appears incremental as it combines existing methods.
The paper tackles object recognition and automatic labeling in large-area remote sensing images by integrating YOLOv11 and MobileNetV3-SSD through ensemble learning, achieving a balance between accuracy and speed while reducing computational demands.
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.