用深度网络自动评烟叶质量，提升工厂管理效率。
InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management
- 定制卷积网络分析烟叶颜色、成熟度和烘烤细节
- 97%准确率，实时判断助力生产决策
- 适合烟草质检与智能制造领域从业者
烟草车间面临烘烤不良、原料不稳、排程混乱和监管缺失等问题，导致成本上升、品质下降。人工检测大量烟叶耗时费力且不可靠。深度卷积神经网络在超越传统方法方面取得进展，但需针对烟叶等级细微差异进行大量定制。本研究提出InspectionV3，一种基于定制深度卷积神经网络的自动化晾晒烟分级系统。通过包含21,113张图像的标注数据集，覆盖20个质量等级，专家对烟叶图像进行清洗、标注与增强。多层CNN结合批量归一化，捕捉透光性、湿度斑点等车间特性，将视觉模式转化为可操作信息。支持实时现场分级，媲美人工专家水平，并通过图像驱动的分析仪表盘追踪产量预测、库存、瓶颈，优化数据决策。经再训练补充更多标注图像后，模型表征能力增强，适应季节变化。评估显示准确率97%、精确率与召回率95%、F1分数与AUC均为96%、特异性95%，验证了实际应用可行性。
原文摘要 · Abstract (English)
The problems that tobacco workshops encounter include poor curing, inconsistencies in supplies, irregular scheduling, and a lack of oversight, all of which drive up expenses and worse quality. Large quantities make manual examination costly, sluggish, and unreliable. Deep convolutional neural networks have recently made strides in capabilities that transcend those of conventional methods. To effectively enhance them, nevertheless, extensive customization is needed to account for subtle variations in tobacco grade. This study introduces InspectionV3, an integrated solution for automated flue-cured tobacco grading that makes use of a customized deep convolutional neural network architecture. A scope that covers color, maturity, and curing subtleties is established via a labelled dataset consisting of 21,113 images spanning 20 quality classes. Expert annotators performed preprocessing on the tobacco leaf images, including cleaning, labelling, and augmentation. Multi-layer CNN factors use batch normalization to describe domain properties like as permeability and moisture spots, and so account for the subtleties of the workshop. Its expertise lies in converting visual patterns into useful information for enhancing workflow. Fast notifications are made possible by real-time, on-the-spot grading that matches human expertise. Images-powered analytics dashboards facilitate the tracking of yield projections, inventories, bottlenecks, and the optimization of data-driven choices. More labelled images are assimilated after further retraining, improving representational capacities and enabling adaptations for seasonal variability. Metrics demonstrate 97% accuracy, 95% precision and recall, 96% F1-score and AUC, 95% specificity; validating real-world viability.
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