CJS-YOLOv5n:一种高性能检测模型,用于检测香烟外观缺陷
Yihai Ma1,2, Guowu Yuan1,2, Kun Yue1,2
1School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
一个新的缺陷检测模型,CJS-YOLOv5n,提高了在高速生产线上的香烟质量控制. 这种先进的你只看一次版本5纳米模型显著提高了缺陷识别的准确性和速度.
科学领域:
- 工业自动化工业自动化
- 计算机视觉 计算机视觉 计算机视觉
- 质量控制 质量控制
背景情况:
- 香烟生产线面临着外观缺陷影响产品质量的挑战.
- 现有的缺陷检测方法在高吞吐量制造中难以平衡精度和速度.
研究的目的:
- 开发一个准确和快速的缺陷检测模型,用于生产线上的香烟外观.
- 解决当前方法在实现每秒高检测速率方面的局限性.
主要方法:
- 拟议的CJS-YOLOv5n模型,集成YOLOv8的C2F模块,跳跃孔卡特和SIoU损失函数.
- 使用YOLOv5n (你只看一次5纳米版本) 作为基础架构.
- 实施了改进,以改善特征提取,轻微缺陷保存和本地化准确性.
主要成果:
- CJS-YOLOv5n的检测速度超过了每秒500 (FPS).
- 该模型将回忆率提高了2.3%,平均平均精度 (mAP) @0.5提高了1.7%.
- 证明了卓越的性能,适用于高速卷烟生产线.
结论:
- 该CJS-YOLOv5n型号在自动卷烟外观缺陷检测方面取得了重大进展.
- 该模型有效地平衡了高速处理与增强的检测精度.
- 这种技术非常适合烟草行业实时质量控制.
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