轻量级的线线检测模型,用于用线银行进行自动连接
Ning Dai1, Xiaohan Hu2, Kaixin Xu3
1Key Laboratory of Modern Textile Machinery and Technology of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, 310018, China. zstudn@zstu.edu.cn.
Scientific reports
|July 12, 2024
概括
一个新的轻量级YOLOv8型号可以有效地检测自动绕机的空线圈. 这种先进的系统通过提高实时检测和定位精度来提高生产效率.
科学领域:
- 织品制造技术 织品制造技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动更换空管对于绕机的效率至关重要.
- 传统的检测方法缺乏灵敏度和适应性.
- 神经网络在实时检测方面可能是计算上昂贵的.
研究的目的:
- 开发一种轻量级和高效的线线圈检测模型.
- 为了改进实时检测和定位空管在自动上.
- 为了降低与基于神经网络的检测相关的计算成本.
主要方法:
- 提出了一种基于YOLOv8架构的轻量级线线圈检测模型.
- 集成的Darknet-53骨干与大型选择性内核单元用于密集的目标.
- 引入了双层路由注意力机制和FasterNeck,以提高功能捕获和速度.
- 使用加权的IOU与动态聚焦用于界限框回归.
主要成果:
- 在定制数据集上实现了94.2%的平均平均精度 (mAP).
- 模型尺寸是紧的4.9 MB,检测速度为223 FPS.
- 在准确性和速度方面,与基线模型相比显著改善.
结论:
- 提出的基于YOLOv8的模型为线线圈检测提供了一个高度准确和高效的解决方案.
- 该模型的轻量级设计和高推断速度满足了工业部署的要求.
- 这项技术提高了自动上机的实时能力和整体效率.
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