一个基于YOLOv8n的优化模型,用于实时检测塔罗条生产中的缺陷

Kan Luo^1,2, Chuanshuai Jia^3,4, Yu Chen^4,5

¹School of electronic, Electrical engineering and Physics, Fujian University of Technology, Fuzhou, 350118, China. luokan@fjut.edu.cn.

Scientific reports

|December 30, 2025

概括

这项研究引入了修改后的YOLOv8n模型,用于自动检测塔罗带缺陷,达到99%以上的准确性,具有高精度和回忆. 高效的深度学习方法提高了工业处理质量和实时能力.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
机器学习机器学习

背景情况:

塔罗加工严重依赖于手工劳动,需要自动检测缺陷以提高效率和质量.
传统的计算机视觉方法在工业环境中难以准确,而深度学习模型可能是计算密集的.

研究的目的:

开发一种高效准确的深度学习模型,用于实时工业环境中自动检测塔罗条的缺陷.
通过优化模型架构和损失函数来解决现有方法的局限性,以提高性能.

主要方法:

一个修改后的YOLOv8n架构,采用双向特征金字塔网络 (BiFPN) 进行增强的特征融合.
集成VoV-GSCSP模块和共享参数检测头,以减少计算复杂性.
利用智能交叉对联 (WIoU) 损失函数和广泛的数据增强,以提高准确性和稳定性.

主要成果:

优化的模型实现了超过99%的平均平均精度 (mAP50),精度和回忆率高于0.99.
与原始YOLOv8n模型 (mAP50: 94.63%) 相比,表现显著改善.
该模型在Raspberry Pi 5上部署时表现出强度和通用性,准确地检测新数据中的缺陷.

结论:

拟议的修改后的YOLOv8n模型提供了卓越的准确性和计算效率,可实时检测塔罗带缺陷.
这种进步非常适合要求高通量质量控制的工业应用.
该研究强调了优化深度学习对农产品加工的潜力.

关键词:

这是BiFPN BiFPN.计算机视觉计算机视觉计算机视觉拉斯伯雷皮是什么意思拉斯伯雷皮是什么意思塔罗皮带缺陷检测检测器的缺陷检测器这是一个VoV-GSCSP.这将是WIOUU的结果.这是一个YOLO YOLO.

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