Zhao Beigeng, Zhao Jiawen, Song Rui, Yu Lizhi, Zhang Xia, Liu Jiren
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
College of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, Liaoning, China.
PLoS One. 2025 Jul 1;20(7):e0321920. doi: 10.1371/journal.pone.0321920. eCollection 2025.
Accurately and rapidly detecting objects and their locations in drone-captured images from maritime search and rescue scenarios provides valuable information for rescue operations. The YOLO series, known for its balance between lightweight architecture and high accuracy, has become a popular method among researchers in this field. Recent advancements in the newly released YOLO11 model have demonstrated significant progress in general object detection tasks across everyday scenarios. However, its application to the specific task of drone-based maritime search and rescue still leaves substantial room for improvement. To address this gap, we propose targeted optimizations to enhance YOLO11's performance in this domain. These include integrating a Space-to-Depth module into the Backbone, incorporating a content-aware upsampling algorithm in the Neck, and adding an extra detection head to better exploit shallow image features. These modifications significantly improve the model's ability to detect small, overlapping, and rarely occurring objects, which are common challenges in maritime search and rescue tasks. Experimental evaluations conducted on the large-scale SeaDronesSee dataset demonstrate that the proposed optimized YOLO11 outperforms YOLOv8, YOLO11, and MambaYOLO across all scales. Moreover, under lightweight configurations, the model achieves substantial performance gains over YoloOW, a method renowned for its accuracy but depends on heavyweight configurations. In the lightweight complexity range, the proposed model achieves a relative accuracy improvement of 20.85% to 43.70% compared to these state-of-the-art methods. The code supporting this research is available at https://github.com/bgno1/sds_yolo11.
在海上搜索和救援场景中，准确、快速地检测无人机拍摄图像中的物体及其位置，可为救援行动提供有价值的信息。以轻量级架构与高精度之间的平衡而闻名的YOLO系列，已成为该领域研究人员中一种流行的方法。新发布的YOLO11模型的最新进展在日常场景中的一般目标检测任务中显示出显著进步。然而，其在基于无人机的海上搜索和救援特定任务中的应用仍有很大的改进空间。为了弥补这一差距，我们提出了有针对性的优化措施，以提高YOLO11在该领域的性能。这些措施包括在主干网络中集成空间到深度模块，在颈部合并内容感知上采样算法，并添加一个额外的检测头，以更好地利用浅层图像特征。这些修改显著提高了模型检测小的、重叠的和罕见出现物体的能力，而这些都是海上搜索和救援任务中的常见挑战。在大规模SeaDronesSee数据集上进行的实验评估表明，所提出的优化后的YOLO11在所有尺度上均优于YOLOv8、YOLO11和MambaYOLO。此外，在轻量级配置下，该模型相对于以准确性著称但依赖于重量级配置的YoloOW方法，实现了显著的性能提升。在轻量级复杂度范围内，与这些先进方法相比，所提出的模型实现了20.85%至43.70%的相对准确性提升。支持本研究的代码可在https://github.com/bgno1/sds_yolo11获取。
