一个新的高效的多对象检测和尺寸计算混合烟草碎片使用改进的YOLOv7网络和LWC算法
Kunming Jia1, Qunfeng Niu1, Li Wang1
1College of Electrical Engineering, Henan University of Technology, Zhengzhou 450000, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种改进的YOLOv7微型模型,用于检测混合烟草碎片,并计算它们在香烟制造中的不间断率. 新模型提高了多对象检测和尺寸计算的准确性,改善了质量检查.
科学领域:
- 计算机视觉和机器学习
- 工业自动化和质量控制
背景情况:
- 准确检测烟草碎片和计算不间断率对于香烟质量检查至关重要.
- 现有的机器视觉方法难以分类微小,形态复杂和重叠的烟草碎片.
研究的目的:
- 为混合烟草碎片开发一个高效的多对象检测模型.
- 为了准确计算使用新的二维尺寸计算算法,不间断的烟草碎片率.
主要方法:
- 开发了一种改进的YOLOv7微型模型,配备了轻量级的Resnet19骨干.
- 像SPPCSPC和检测头这样的关键组件被SPPFCSPC和一个脱的联合检测头所取代.
- 提出了一种新的长宽计算 (LWC) 算法,用于2D尺寸确定单个碎片的尺寸.
主要成果:
- 该模型实现了高检测精度 (0.883) 和mAP (0.932@.5).
- 测试时间显著缩短至4.12 ms.
- 平均长度和宽度检测精度分别达到-1.7%和13.2%,与手动检查标准保持一致.
结论:
- 开发的模型提供了一个有效的解决方案,用于多对象检测和卷烟生产中混合烟草碎片的尺寸计算.
- 这种方法为加强烟草行业的自动化质量检查和类似的混合图像分析任务提供了一个强大的方法.
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