GOI-YOLOv8分组偏移和孤立的长 Det 低光目标检测
Mengqing Mei1, Ziyu Zhou1, Wei Liu1
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究介绍了GOI-YOLO,一种新的物体检测模型,可以显著提高低光条件下的性能. 新模型增强了功能融合和效率,在具有挑战性的数据集上表现优于现有方法.
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:
- 对象检测通常需要良好的可见度,限制其在低光下使用.
- 由于数据差异,现有的YOLO系列模型在低光条件下扎着性能下降.
研究的目的:
- 开发一个在低可见性环境中表现出色的物体检测模型.
- 为了增强YOLOv8架构,以改进低光物体检测.
主要方法:
- 基于YOLOv8.8的YOLO (GOI-YOLO) 目标检测-YOLO (GOI-YOLO) 的拟议分组偏移和隔离长鹿.
- 集成的repGFPN特征金字塔网络,采样地图偏移用于增强特征融合.
- 员工组卷积以隔离干扰并提高效率.
主要成果:
- 在低光条件下,GOI-YOLO实现了卓越的性能.
- 与YOLOv8.8相比,参数数量减少了11%,计算要求减少了28%.
- 在ExDark数据集上,Map50增长了2.1%,Map95增长了0.6%.
结论:
- 在低光情况下,GOI-YOLO为实时物体检测提供了显著的进步.
- 该模型证明了提高效率和准确性,解决了当前方法的关键局限性.
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