基于改进Faster RCNN的工业机器人分拣系统

Industrial Robots Sorting System Based on Improved Faster RCNN

传统的分拣作业无法伴随工作环境的变化进行相应的调整,针对此种不足,出现了基于机器视觉的分拣机器人的相关研究,通过将图像处理和特征工程技术引入视觉模块,使得分拣系统能适时的调整.不同于这些方法,本研究基于实验室的工业分拣系统,将深度学习方法应用其中.通过将Faster RCNN检测算法引入视觉模块并对区域提取网络RPN进行相关改进,加快Faster RCNN模型的检测过程,使得该系统满足工业的实时性要求.Faster RCNN作为一种端到端的方法,能自动对输入图像生成更具表达力的特征,对相应目标提取相应特征,这避免了人工设计特征,它的特征自动生成能力使其能适用于各种场景,这提升了工业分拣机器人的环境适应能力.

Traditional sorting operation can not be adjusted with the change of working environment.In view of this shortcoming,a sorting robot is researched based on machine vision.By introducing image processing and feature engineering technology into the visual module,the sorting system can be adjusted in time.Unlike these methods,this research is based on the industrial sorting system in the laboratory and applies the deep learning method to it.By introducing faster RCNN detection algorithm into visual module and improving of Region Proposal Network(RPN),the detection process of faster RCNN model is accelerated,so that the system meets the real-time requirements of industry.faster RCNN,as an end-to-end method,can automatically generate more expressive features for input images and extract corresponding features for corresponding targets.This avoids the manual design features.Its automatic feature generation ability makes it suitable for various scenarios,which improves the environmental adaptability of industrial sorting robots.