论文标题
使用修剪的重新NET型号识别有缺陷的矿物羊毛
Recognition of Defective Mineral Wool Using Pruned ResNet Models
论文作者
论文摘要
矿物质羊毛生产是一个非线性过程，使得很难控制最终质量。因此，使用一种非破坏性方法来分析产品质量并识别有缺陷的产品至关重要。为此，我们开发了一种用于矿物质羊毛的视觉质量控制系统。收集羊毛标本的X射线图像，以创建一套有缺陷和非缺陷样品的训练集。之后，我们基于Resnet体系结构开发了几种识别模型，以找到最有效的模型。为了使现实生活中的轻巧和快速的推理模型，将两种结构修剪方法应用于分类器。考虑到数据集的数量低，在训练过程中使用了交叉验证和增强方法。结果，我们获得了一个超过98％精度的模型，与公司使用的当前程序相比，它可以识别出20％的缺陷产品。
Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a non-destructive method to analyze the product quality and recognize defective products is critical. For this purpose, we developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. Afterward, we developed several recognition models based on the ResNet architecture to find the most efficient model. In order to have a light-weight and fast inference model for real-life applicability, two structural pruning methods are applied to the classifiers. Considering the low quantity of the dataset, cross-validation and augmentation methods are used during the training. As a result, we obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.
