无人机 (UAV) 的高空成像能力为海上搜索和救援 (SAR) 作业提供了有效的解决方案。在此类任务中,准确识别图像中的船只、人员和物体至关重要。虽然在一般图像数据集上训练的目标检测模型可以直接应用于这些任务,但由于海上 SAR 场景的具体特征带来的独特挑战,其有效性受到限制。为了应对这一挑战,我们的研究利用特定于基于无人机的海上 SAR 的大型基准数据集 SeaDronesSee 来分析和探索该场景中图像数据的独特属性。我们确定在这种情况下检测特定类别的难以检测对象时需要进行优化。在此基础上,提出了一种基于聚类分析的锚框优化策略,旨在提高著名的两阶段目标检测模型在这一专门任务中的性能。进行了实验来验证所提出的锚框优化方法并探讨其有效性的根本原因。实验结果表明,我们的优化方法比 torchvision 的默认锚框配置和 SeaDronesSee 官方示例代码配置分别实现了 45.8% 和 10% 的平均精度提高。这种增强尤其明显,因为该模型在 SeaDronesSee 数据集的 SAR 场景中检测船上游泳者、漂浮物和救生衣的能力显着提高。 本研究的方法和结果有望为基于无人机的海上SAR研究界提供数据特征和模型优化的宝贵见解,为未来的研究提供有意义的参考。
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Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR
The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model’s significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset’s SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research.