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基于机器视觉与Faster-RCNN的Delta机器人工件识别检测
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山西省重点国际科技合作项目(201903D421015)


Workpiece Recognition and Detection of Delta Robot Based on Machine Vision and Faster-RCNN
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    摘要:

    针对传统并联机器人在工作环境中存在抓取不精确、定位与分类识别效率低下的问题,提出一种基于机器视觉与Faster-RCNN神经网络的工件识别检测技术。采用Delta机器人实验平台采集图像,进行图像的预处理操作并将其添加至网络训练集。通过Python3.7-torch1.7搭建深度学习中的Faster R-CNN卷积神经网络,作为基本框架训练工件图像数据集。最后将训练后的卷积神经网络得到的工件检测结果与原实验工件识别系统对比。结果表明:改进后的识别平均精确度比原有识别系统有所提高,反应时间缩短,并且能识别不同类型的工件。

    Abstract:

    In order to solve the problems of inaccurate grasping and low efficiency of localization and classification in the working environment of the traditional parallel robots,a detection technique of workpiece recognition based on machine vision and Faster-RCNN neural network was proposed.they were collected by the parallel robot experimental platform,they were preprocessed then added to the network data set.The Faster-RCNN convolutional neural network in depth learning with Python 3.7-torch 1.7 was built as the basic framework for training workpiece image data sets.Finally,the workpiece detection results obtained by the trained convolutional neural network were compared with the original test workpiece identification system.The results show that the improved recognition average accuracy is better than the original recognition system,the reaction time is shortened and different types of workpieces can be recognized.

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张宇廷.基于机器视觉与Faster-RCNN的Delta机器人工件识别检测[J].机床与液压,2023,51(5):35-40.
ZHANG Yuting. Workpiece Recognition and Detection of Delta Robot Based on Machine Vision and Faster-RCNN[J]. Machine Tool & Hydraulics,2023,51(5):35-40

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  • 在线发布日期: 2023-04-07
  • 出版日期: 2023-03-15