Industrial product quality assessment using deep learning with defect attributes

Abstract

Industrial defect quantification refers to the process of quantifying various defects that occur during the production of industrial products, with the aim of achieving precise grading of product quality. This process is a key component in the quality monitoring phase of production. The quantification of defects faces challenges such as accurately describing the appearance of defects and difficulty in dividing product grades. In response to these issues, we propose a Defect Attribute-based Quantitative Assessment Network (DQANet). It employs a Refined Feature Attention Module (RFAM) to guide the flow of information and fusion of features during the encoding process and uses deformable convolution at the end of the encoder to extract richer contextual information while effectively suppressing noise. We conducted a quantitative evaluation of defects and annotated quality grades on the Mobile Screen Defect (MSD) dataset to train and validate the performance of our model. The experimental results indicate that our method has achieved promising performance. In addition, this work applies the defect attribute-based quantification assessment network to the tasks of defect classification and segmentation. Experimental results show that defect quantification assessment can provide effective auxiliary information, helping the network understand defect features more accurately and improve the performance of related visual tasks.