19.76MBZIP

从图像中检测和定位制造缺陷

使用 ResNet50 深度学习模型预测钢板上的缺陷，并使用 Res-UNET 模型直观地定位缺陷。

该项目旨在从图像中预测钢板的表面缺陷。这种计算机视觉技术利用预训练的 ResNet50 模型利用迁移学习。如果检测到默认值，另一个模型允许在图像上直观地显示检测到的默认值（图像分割）。第二个模型使用 Res-U-net 架构生成逐像素预测以定位图像上的缺陷。这些模型是使用 tensorflow Keras 开发的。

Project Description

该项目包括两个步骤：

分类模型的训练和评估，以确定钢板是否具有表面默认值。该模型处理制造钢板的图片，并利用预先训练的 ResNet50 模型使用临时训练数据集针对目标问题进行微调。零件有 4 种缺陷类型，但第一步将零件分类为有缺陷/无缺陷。在接下来的图像分割期间执行缺陷类型的确定。

训练和测试图像分割以定位有缺陷的纸张上的缺陷。这一步使用 U-net 模型来预测图像的每个像素是否是表面默认值的一部分。输出是模型识别的缺陷的可视化。该模型是在临时数据集上训练的。此外，该模型还可以预测 4 个可能类别之间的缺陷类型。

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