Zhao Beigeng, Zhou Ye, Song Rui, Yu Lizhi, Zhang Xia, Liu Jiren
School of Computer Science and Engineering, Northeastern University, Shenyang, China.
College of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang, China.
Sci Rep. 2024 Oct 18;14(1):24492. doi: 10.1038/s41598-024-75807-1.
The task of UAV-based maritime rescue object detection faces two significant challenges: accuracy and real-time performance. The YOLO series models, known for their streamlined and fast performance, offer promising solutions for this task. However, existing YOLO-based UAV maritime rescue object detection methods tend to prioritize high accuracy, often at the expense of real-time performance and ease of implementation and expansion. This study proposes a modular plug-and-play optimization approach based on the YOLOv8 framework, aiming to enhance real-time performance while maintaining high accuracy for UAV maritime rescue object detection. The proposed optimization modules are flexible, easy to implement, and extendable. In experiments on the large-scale publicly available SeaDronesSee dataset, our method achieved a 13.53% improvement in accuracy over YOLOv8x while reducing computational cost by 85.63%. Additionally, it surpassed the detection speed of the SeaDronesSee official code's two-stage detector by over 20 times, while maintaining comparable accuracy. Furthermore, our analysis of the experimental results highlights differences in detection difficulty among various objects and potential biases within the dataset.
准确性和实时性能。以其精简快速的性能而闻名的YOLO系列模型为这项任务提供了有前景的解决方案。然而，现有的基于YOLO的无人机海上救援目标检测方法往往优先考虑高精度，常常以牺牲实时性能以及实现和扩展的简易性为代价。本研究提出了一种基于YOLOv8框架的模块化即插即用优化方法，旨在提高无人机海上救援目标检测的实时性能，同时保持高精度。所提出的优化模块灵活、易于实现且可扩展。在大规模公开可用的SeaDronesSee数据集上进行的实验中，我们的方法在准确性方面比YOLOv8x提高了13.53%，同时将计算成本降低了85.63%。此外，它的检测速度比SeaDronesSee官方代码的两阶段检测器快20倍以上，同时保持了相当的准确性。此外，我们对实验结果的分析突出了各种物体之间检测难度的差异以及数据集中的潜在偏差。
