This paper presents a novel approach to tracking ships in Synthetic Aperture Radar (SAR) images based on an improved lightweight YOLOv8 Nano (YOLOv8n), specially devised to improve efficiency without compromising accuracy. In our method, we replaced the heavy backbone and neck of YOLOv8 with HGNetv2 and slim-neck, respectively. We also implemented a lightweight decoupling head using EMSConvP. Additionally, we integrated a knowledge distillation module to further enhance detection capabilities. Furthermore, we conducted extensive experiments on the short-time sequence SAR dataset to demonstrate superior accuracy metrics compared to the original YOLOv8n model. Regarding tracking ships in SAR images, we developed a multi-object tracking (MOT) technique called Cascaded-Buffered IoU (C-BIoU). This method enlarges the detection and trajectory matching space by increasing the buffer zone, effectively combining detection and trajectory information from short-time sequence SAR images. The findings reveal that our method significantly reduces the computational complexity, parameters, and model size by up to 54.7 %, 68.4 %, and 68.3 %, respectively, with respect to the original model metrics. As a direct consequence of these reductions, our proposed model demonstrates a remarkable 133.1 % improvement in image processing speed expressed as frames per second (FPS). Moreover, Our C-BIoU method shows outstanding performance in tracking accuracy and efficiency, with superior Higher Order Tracking Accuracy (HOTA), Multiple Object Tracking Precision (MOTP), and Identification F1 score (IDF1) scores of 72.8 %, 87.9 %, and 80.7 %, respectively, compared to existing tracking algorithms. The results from testing on multiple datasets highlight our method’s excellent performance in ship detection and tracking, offering high-speed processing capabilities with an average image processing speed of 81 FPS. In this sense, this method provides reliable real-time monitoring and management of maritime traffic, enhancing situational awareness for maritime operations.

中文翻译：

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YOLOShipTracker：使用轻量级 YOLOv8 跟踪 SAR 图像中的船舶


本文提出了一种基于改进的轻量级 YOLOv8 Nano (YOLOv8n) 的合成孔径雷达 (SAR) 图像中跟踪船舶的新方法，该方法是专门为提高效率而不影响精度而设计的。在我们的方法中，我们分别用 HGNetv2 和 slim-neck 替换了 YOLOv8 的重型主干和颈部。我们还使用 EMSConvP 实现了轻量级去耦头。此外，我们还集成了知识蒸馏模块，以进一步增强检测能力。此外，我们对短时序列 SAR 数据集进行了广泛的实验，以证明与原始 YOLOv8n 模型相比具有更高的准确性指标。关于在 SAR 图像中跟踪船舶，我们开发了一种称为级联缓冲 IoU (C-BIoU) 的多目标跟踪 (MOT) 技术。该方法通过增加缓冲区扩大了检测和轨迹匹配空间，有效结合了短时序列SAR图像的检测和轨迹信息。研究结果表明，与原始模型指标相比，我们的方法将计算复杂性、参数和模型大小分别显着降低了 54.7%、68.4% 和 68.3%。作为这些减少的直接结果，我们提出的模型展示了以每秒帧数 (FPS) 表示的图像处理速度显着提高了 133.1%。此外，我们的C-BIoU方法在跟踪精度和效率方面表现出色，高阶跟踪精度（HOTA）、多目标跟踪精度（MOTP）和识别F1分数（IDF1）得分分别为72.8%、87.9%和与现有跟踪算法相比，分别为 80.7%。 多个数据集的测试结果突显了我们的方法在船舶检测和跟踪方面的出色性能，提供了平均图像处理速度为 81 FPS 的高速处理能力。从这个意义上说，该方法提供了可靠的海上交通实时监控和管理，增强了海上作业的态势感知能力。