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Bibliographic Details
Main Author: Dong, Wuzheng
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2411.07802
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author Dong, Wuzheng
author_facet Dong, Wuzheng
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2411_07802
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Large-scale Remote Sensing Image Target Recognition and Automatic Annotation
Dong, Wuzheng
Computer Vision and Pattern Recognition
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.
title Large-scale Remote Sensing Image Target Recognition and Automatic Annotation
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2411.07802