YOLO-ISTD:一种基于YOLOv5-S的红外小目标检测方法
Ziqiang Hao1, Zhuohao Wang1, Xiaoyu Xu1
1National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China.
PloS one
|June 13, 2024
概括
这项研究介绍了YOLO-ISTD,一种改进的红外小目标检测方法. 它增强了特征提取和融合,大大提高了在具有挑战性的环境中对弱目标的检测精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 红外成像技术 红外成像技术
背景情况:
- 红外目标检测对于环境监测和自动驾驶等工业应用至关重要.
- 由于信息和上下文有限,在红外图像中检测小,弱的目标存在重大挑战.
研究的目的:
- 开发用于红外小型目标检测 (ISTD) 的改进方法.
- 为了提高在红外图像中检测弱目标的准确性和可靠性.
主要方法:
- 提出了YOLO-ISTD,这是一个增强的YOLOv5-S框架,包含了一个新的Shuffle Attention CSP (SACSP) 功能提取模块.
- 引入了一个非本地SPPF (NL-SPPF) 模块,以改进远程特征捕获和相关性.
- 开发了一种修改后的K-means集群算法 (K-means_DIOU),以更好地生成.
- 修改的检测头用于更细粒度的特征分析.
主要成果:
- YOLO-ISTD在NUST-SIRST数据集上取得了显著的改进,mAP@0.5增加了8.568%,mAP@0.95增加了8.618%.
- 与现有模型相比,该方法有效减少了错误检测和错误报警.
- 在精度,回忆和模型融合速度方面取得了显著的收益.
结论:
- 拟议的YOLO-ISTD方法为红外小目标检测提供了一个强大的解决方案.
- 集成的SACSP和NL-SPPF模块以及K-means_DIOU显著提高了检测性能.
- 这项工作有助于提高红外监控和监控系统的能力.
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