大跨度尺寸和不规则形状的目标检测方法使用可变卷积改进的YOLOv8
Yan Gao1, Wei Liu2, Hsiang-Chen Chui3
1School of Intergated Circuits, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了改进的YOLOv8物体检测模型,以提高对不规则和小目标的准确性和效率. 改进后的模型在检测具有挑战性的样品方面取得了更好的性能,这对于实时工业检查至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 对象检测模型经常与不规则的形状,小的或重叠的目标作斗争.
- 现有的方法面临着低分辨率标签,背景噪音和计算复杂性的挑战.
研究的目的:
- 开发一种改进的YOLOv8物体检测方法,以提高准确性和效率.
- 解决检测跨度,不规则形状和小目标的局限性.
主要方法:
- 将可变形的卷积模块集成到YOLOv8骨干中,以改善目标感知.
- 集成了Sim-AM (简单无参数注意力机制) 模块,以增强功能注意力和减少计算负载.
- 取代空间金字塔聚合与焦调节网络,以简化模型结构和加快检测速度.
主要成果:
- 改进的YOLOv8模型显示平均精度 (AP) 增加了2.1%,平均精度 (mAP) 增加了0.8%.
- 实现了每秒5.4 (FPS) 的减少,表明检测速度有所改善.
- 在废钢数据集上的实验验证证证了该模型对多样化和具有挑战性的目标的有效性.
结论:
- 拟议的可变卷积改进的YOLOv8有效地提高了复杂的工业检查任务的对象检测精度和效率.
- 整合可变形卷积和Sim-AM模块,以及简化的网络结构,为实时检测不规则和小物体提供了强大的解决方案.
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