模块化YOLOv8优化实时无人机海上救援物体检测
Beigeng Zhao1,2, Ye Zhou2, Rui Song2
1School of Computer Science and Engineering, Northeastern University, Shenyang, China.
Scientific reports
|October 18, 2024
概括
本研究介绍了一个模块化的YOLOv8优化无人机 (UAV) 海上救援物体检测,显著提高准确性和实时性能,同时降低计算成本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 海上安全的航行.
背景情况:
- 基于无人机的海上救援物体检测需要高精度和实时性能.
- 现有的基于YOLO的方法往往会为了准确性而牺牲速度,或者很难实现.
- 在海上搜索和救援方面,需要有效和适应性的解决方案.
研究的目的:
- 为YOLOv8框架开发一个模块化,插件和操作的优化.
- 为了提高无人机海上救援物体检测的实时性能和准确性.
- 为实际应用创造一个灵活,可扩展的解决方案.
主要方法:
- 在YOLOv8架构上实施模块化优化策略.
- 使用SeaDronesSee数据集进行大规模实验.
- 与基线YOLOv8x和现有的两级探测器进行比较分析.
主要成果:
- 与YOLOv8x.相比,实现了13.53%的精度改进.
- 减少了85.63%的计算成本.
- 超过两级探测器的检测速度超过20倍,准确度可比.
结论:
- 拟议的模块化方法有效地平衡了无人机海上救援的准确性和实时性能.
- 该方法提供了灵活性,易于实施和可扩展性.
- 数据集分析揭示了对象特定的检测挑战和潜在的偏见.
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