通过YOLOv9s在低可见度雾条件下增强对象检测
Yang Zhang1, Bin Zhou1, Xue Zhao1
1School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China.
PloS one
|February 24, 2025
概括
这项研究使用具有对比学习和注意力机制的YOLOv9s增强了在低可见度雾中对象检测. 改进的框架在具有挑战性的雾条件下提高了检测精度和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 由于对比度低和亮度高,低可见度雾环境会降低对象检测性能.
- 在这样的条件下,传统的算法在精度和稳定性方面扎.
研究的目的:
- 为YOLOv9s引入一个增强的物体检测框架,专门为低可见度雾设计.
- 为了提高在雾条件下对象检测的准确性和实时性能.
主要方法:
- 集成的Patchwise对比学习来增强本地特征细节和减少干扰.
- 集成有效的多尺度注意力和智能IoU动态聚焦机制,用于全球质量优化.
- 实施了一个非单调的战略,用于动态损失函数重量调整.
主要成果:
- 拟议的算法显著提高了检测精度,回忆和平均平均精度 (mAP).
- 对COCO2017雾增强数据集的实验评估显示,与最先进的技术相比,其性能优越.
- 该框架实现了对道,空间和位置信息的增强敏感性.
结论:
- 增强的YOLOv9s框架有效地解决了在低可见度雾物体检测方面的挑战.
- 对比学习和注意力机制的结合导致了更高的检测性能和效率.
- 开发的算法为需要在恶劣天气中检测物体的现实应用提供了强大的解决方案.
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