Foreign objects detection using deep learning techniques for graphic card assembly line

Abstract

An assembly is a process in which operators and machines manufacture products from semi-finished components into finished goods. It is important to conduct quality control at the end of the assembly line and ensure that no foreign object is put on the conveyor. This study uses a case of foreign object detection in graphics card assembly line to create models which is capable of detecting and marking foreign objects using convolutional neural network (CNN) models. This study uses Inception Resnet v2 to conduct the foreign object classification and Attention Residual U-net++ for the foreign object segmentation. Both benchmark datasets and case study dataset are employed for model evaluation. The result shows that the proposed models can have more promising result than some existing models.