YOLOv5_mamba:基于双向密集反网络和自适应门功能融合的无人驾驶飞行器物体检测
Shixiao Wu1, Xingyuan Lu2, Chengcheng Guo3,4
1School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
Scientific reports
|September 27, 2024
概括
本研究介绍了YOLv5_mamba,这是一个改进的无人机 (UAV) 对象检测系统. 它通过集成先进的网络模块显著提高了空中图像中小物体检测的准确性.
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:
- 在无人驾驶飞行器 (UAV) 空中图像中检测小物体存在挑战,因为特征信息有限.
- 现有的检测算法对空中数据集中的小物体表现不佳.
研究的目的:
- 开发一个增强的无人机空中物体检测系统,YOLv5_mamba,以解决目前小型物体检测方法的局限性.
- 提高无人机拍摄的空中图像中对象检测的准确性和能力.
主要方法:
- 拟议的YOLv5_mamba系统通过将YOLOv8的C2f模块纳入脊柱来增强YOLOv5算法,以改善特征提取.
- 引入了一个双向密集的反网络,集成mamba和C2f模块,以支持部架构中的上下文信息传输.
- 开发了一个自适应性门功能融合网络,以改进YOLOv5的头部,从而提高最终检测性能.
主要成果:
- 在VisDrone2019数据集上,YOLv5_mamba在检测准确度上比基线YOLOv5.5提高了9.3%.
- 与YOLOv5-s.相比,该系统在UCAS_AOD数据集上显示了9%的性能增长,在DIOR数据集上显示了12%的性能增长.
- 实验结果证实了空中图像中小物体的优越检测性能.
结论:
- YOLv5_mamba有效地解决了无人机空中图像中小物体检测的挑战.
- 双向密集反网络和自适应门功能融合的整合显著提高了检测准确性和能力.
- 拟议的系统提供了一个有前途的解决方案,用于在空中监视和分析中改进对象识别.
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