I-YOLOv11n:用于无人机空中图像的轻量级和高效的小型目标检测框架
Yukai Ma1, Caiping Xi1, Ting Ma1
1College of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了I-YOLOv11n,这是一种轻量级算法,用于无人机 (UAV) 小目标检测. 它提高了准确性,并减少了模型大小,以便在关键应用中获得更好的实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 无人机 (UAV) 应用需要准确和实时检测小目标.
- 现有的算法在小目标表示,计算开销和部署适应性方面扎.
研究的目的:
- 提出一种轻量级算法,I-YOLOv11n,用于改进无人机小型目标检测.
- 为了提高特征表示和压缩模型结构以提高效率.
主要方法:
- 设计的RFCBAMConv模块结合了可变形卷积和通道空间注意力.
- 开发了STCMSP上下文多层次金字塔和变压器-DyHead混合检测头.
- 实施协作轻量化战略,包括知识蒸,道修剪和脚优化.
主要成果:
- 实现了3.87M参数和14.7GFLOP的模型大小.
- 与YOLOv11n.相比,检测准确度提高了,mAP@0.5和mAP@0.5:0.95分别增加了7.1%和4.9%,与YOLOv11n.相比.
- 保持了实时处理能力.
结论:
- I-YOLOv11n在准确性,轻量化设计和部署适应性方面提供了显著的优势,用于无人机小型目标检测.
- 提出的方法有效地解决了现有算法的局限性.
- 在VisDrone,AI-TOD和SODA-A数据集上验证了性能.
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