Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

中文翻译：

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YOLOv8-BYTE：利用短时序列 SAR 图像进行船舶跟踪算法，利用 GeoAI 进行灾难响应

船舶跟踪技术对于发生灾难时的紧急救援至关重要。快速识别船只的位置和状态对于救援队能够在灾区高效部署至关重要。在应对突发事件或自然灾害时，船舶跟踪技术在支持应急救援行动和资源分配、提高海上运输系统的整体弹性方面发挥着关键作用。然而，多目标跟踪（MOT）算法的研究主要集中在光学图像数据集上。相比之下，合成孔径雷达 (SAR) 的图像数据面临着独特的挑战，例如散焦干扰、高误报率和缺乏先验样本。为了克服这些特殊的挑战，我们提出了一种针对 SAR 图像开发的鲁棒 MOT 算法，以在困难的成像条件下实现有效的多血管跟踪。特别是，我们通过引入基于扩散模型的数据增强训练方法来优化 YOLOv8 检测网络。该方法提高了网络对缩放、旋转和平移变形的鲁棒性。此外，提出了一种增强的swin变压器作为特征提取网络，增强了检测网络的表示能力。此外，通过直接捕获跟踪矩形框的高度和宽度的细节来增强 KF 技术中的状态参数。此次对ByteTrack算法的细化，旨在实现跟踪矩形与船舶更加精准的拟合，进一步提高整体跟踪性能。船舶检测和多目标跟踪数据集的实验结果显示了所提出模型的令人印象深刻的性能。该模型的准确率为97.60%，召回率为96.36%，平均准确率为96.72%，模型参数减少了18%，实现了优异的检测精度。此外，HOTA、MOTA 和 IDF1 等关键跟踪指标也有了显着改进，与基线算法相比分别提高了 4.8%、8.5% 和 6.8%，同时 IDS 显着降低了 37.5%。值得注意的是，跟踪器实时工作，平均分析速度为每秒 47 帧。所提出的 MOT 算法在短时间序列的 SAR 图像数据集上实现了最先进的跟踪性能。因此，所提出的方法是 SAR 图像中船舶跟踪的一个引人注目的解决方案。