LCW-YOLO:一种基于YOLOv11的轻量级多尺度物体检测方法,并在复杂的自然场景中评估其性能
1School of Computer Science and Technology, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
通过保存细节和高效地融合特征,LCW-YOLO增强了对象检测. 这种轻量级的框架提高了准确性和速度,特别是在复杂场景中的小物体.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 对象检测对于计算机视觉至关重要.
- 在复杂的背景中检测小物体是很困难的,因为特征损失和低效率.
研究的目的:
- 介绍LCW-YOLO,一个轻量级的物体检测框架.
- 提高在具有挑战性的条件下检测小物体的准确性和推断速度.
主要方法:
- 使用波段聚合用于多频重建,以保存细节和减少噪音.
- 使用CGBlock增强的C3K2结构,以有效地融合浅层和深层特征.
- 整合了改进的LDHead,用于增强分类和本地化.
主要成果:
- 在公共数据集上,LCW-YOLO在准确性和速度上优于现有的探测器.
- 在小物体,稀疏和杂乱的环境中表现出显著的优势.
- 在复杂的条件下通过高效的功能融合实现更强大的表示.
结论:
- 拟议的方法推进了资源高效的检测模型.
- LCW-YOLO适用于安全关键和实时应用.
- 维护多频特征和高效的融合是强大的物体检测的关键.
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