mlsanityData to 14 Oct 2026

LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic Annotation

arXiv 2411.15808 ↗First posted 24 Nov 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 24 Nov 2024.

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9 of 23 references.

References · 9

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  4. 2019Searching for MobileNetV3cited by 1,994
  5. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  6. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 425
  7. 2018xView: Objects in Context in Overhead Imagerycited by 146
  8. 2021Focal and Efficient IOU Loss for Accurate Bounding Box Regressioncited by 57
  9. 2019HBONet: Harmonious Bottleneck on Two Orthogonal Dimensionscited by 13

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