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<p>Accurate object detection in drone-captured images is crucial for maritime search and rescue missions. To address the challenges posed by sample constraints and data imbalance in existing datasets, we introduce a greedy algorithm-based dataset rebalancing method tailored for the SeaDronesSee v1 and v2 datasets. Our method aims to alleviate potential sample constraints by redistributing samples between the training and validation sets. To validate the effectiveness of our approach, we trained and evaluated four state-of-the-art object detection models—Faster Region-based Convolutional Neural Network (Faster R-CNN), Cascade R-CNN, You Only Look Once version 8 (YOLOv8), and YOLO11—on both the original and rebalanced versions of the datasets. The results demonstrate that dataset rebalancing significantly improves the performance of all models, with YOLO models benefiting more than their two-stage counterparts. Our contributions include analyzing the sample constraints in the SeaDronesSee series datasets, proposing a greedy algorithm-based rebalancing strategy, and validating its effectiveness through experiments. This study optimizes the sample distribution within each dataset, enhancing model training and generalization, and provides valuable insights for addressing data scarcity issues in similar scenarios. The code supporting this study is available at <a href="https://github.com/bgno1/sds_rebalance">https://github.com/bgno1/sds_rebalance</a>.</p>

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Optimizing drone-captured maritime rescue image object detection through dataset rebalancing under sample constraints

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Beigeng Zhao,
  • Jiaman Li,
  • Jiawen Zhao,
  • Lizhi Yu,
  • Xia Zhang,
  • Jiren Liu

摘要

Accurate object detection in drone-captured images is crucial for maritime search and rescue missions. To address the challenges posed by sample constraints and data imbalance in existing datasets, we introduce a greedy algorithm-based dataset rebalancing method tailored for the SeaDronesSee v1 and v2 datasets. Our method aims to alleviate potential sample constraints by redistributing samples between the training and validation sets. To validate the effectiveness of our approach, we trained and evaluated four state-of-the-art object detection models—Faster Region-based Convolutional Neural Network (Faster R-CNN), Cascade R-CNN, You Only Look Once version 8 (YOLOv8), and YOLO11—on both the original and rebalanced versions of the datasets. The results demonstrate that dataset rebalancing significantly improves the performance of all models, with YOLO models benefiting more than their two-stage counterparts. Our contributions include analyzing the sample constraints in the SeaDronesSee series datasets, proposing a greedy algorithm-based rebalancing strategy, and validating its effectiveness through experiments. This study optimizes the sample distribution within each dataset, enhancing model training and generalization, and provides valuable insights for addressing data scarcity issues in similar scenarios. The code supporting this study is available at https://github.com/bgno1/sds_rebalance.