计算机工程与应用2019，Vol.55Issue(2)：266-270,5.DOI:10.3778/j.issn.1002-8331.1709-0268
基于深度学习的工业视觉检测系统
Industrial Vision Inspection System Based on Deep Learning
摘要
Abstract
Several object detection problems inevitably occur when the product components are detected in the packaging procedure of the traditional industrial production line, including slow detection speed, low-level automation, and low detection accuracy. To solve these problems, a vision-based industrial objects detection system is established to automati-cally inspect the product components, which is based on deep learning. Firstly, an experimental platform is designed to obtain the images containing product components. The convolution layer structures shared by the region proposal network and region convolutional neural network are modified. Thus, a novel object detection method is proposed to accurately locate the product components, which can adaptively detect different product components with different shapes and different sizes by means of an end-to-end mode. The experimental results show that the established system is superior to the existing detection methods, which has the advantages of high detection speed and high detection accuracy.关键词
工业零部件/深度学习/RPN+RCNN检测网络/定位检测Key words
industrial objects/deep learning/Region Proposal Network and Region Convolutional Neural Network (RPN+RCNN)/location detection分类
信息技术与安全科学引用本文复制引用
晋博,蔡念,夏皓,林健发..基于深度学习的工业视觉检测系统[J].计算机工程与应用,2019,55(2):266-270,5.基金项目
国家自然科学基金(No.91648108) （No.91648108）
广东省科技计划项目(No.2015B010124001,No.2015B010104006) （No.2015B010124001,No.2015B010104006）
广州市产学研协同创新重大专项项目(No.201604016064). （No.201604016064）
