一种基于YOLOv5聚变功能增强功能的新型低光物体检测方法
Daxin Peng1,2, Wei Ding3, Tong Zhen4,5
1College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China.
Scientific reports
|February 23, 2024
概括
这项研究介绍了NLE-YOLO,这是一种用于低光物体检测的新型网络,通过减少噪音和增强功能来提高准确性. 基于YOLOv5的增强型号在具有挑战性的低照明条件下表现出色.
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:
- 低亮度物体的检测存在重大挑战,因为照明和噪音不足.
- 现有的方法往往在模糊的环境中难以准确和性能.
研究的目的:
- 开发一个有效的网络,NLE-YOLO,以在低光条件下进行强大的物体检测.
- 为了增强YOLOv5架构的功能提取能力,用于低光场景.
主要方法:
- 推出了NLE-YOLO,这是一个修改后的YOLOv5网络,包含新的模块:C2fLEFEM,AMC2fLEFEM和AMRFB.
- C2fLEFEM集成了低频增强和功能融合,用于降噪和信息保存.
- AMC2fLEFEM利用注意力机制来进行多尺度的特征提取和适应亮度变化的能力,而AMRFB则使用心腔卷积来扩大受体场.
主要成果:
- 在Exdark数据集上的实验表明,与现有方法相比,检测准确度和性能更高.
- 拟议的网络有效地抑制高频噪声,并增强关键目标特征.
- NLE-YOLO显示了对不同亮度的更好的适应性,以及对目标和背景的更好的区分.
结论:
- 在低光物体检测方面,NLE-YOLO提供了显著的进步.
- 这些新型模块有效地解决了噪声干扰,并在暗暗的环境中具有提取限制.
- 该方法为需要在照明不足的情况下可靠检测对象的现实应用提供了有前途的解决方案.
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