基于迁移学习的机器人视觉识别与分拣策略
|
摘要
针对传统工业机器人辨识复杂工件困难、识别度单一等问题,提出一种基于迁移学习的视觉识别与分拣策略。高精度工业相机拍摄到的图片经过HALCON软件图像膨胀、腐蚀等处理之后,导入Pytorch中的神经网络模型,利用迁移学习对目标进行识别分类,最终实现工业机器人智能分拣的目的。实验中,在UR5机器人平台上以形状多变的两种菇类为对象进行迁移学习,进而完成识别及分拣。实验结果表明该策略具备良好的准确性和稳定性。
In order to solve the application problems facing traditional industrial robots such as difficulty in identifying complex industrial parts and singleness in recognition, a visual recognition and sorting strategy based on transfer learning is proposed. Firstly, the pictures taken by high-precision industrial camera are processed by Haclon software, such as expansion, corrosion and then imported to Pytorch which has prepared a good neural network model to identify the target classification, and finally the purpose of industrial robot sorting is achieved. In the experiment, two kinds of mushrooms with varied shapes are taken as an example to implement the transfer learning and sorting on the UR5 robot platform. The experimental results illustrate the good accuracy and stability of the proposed algorithm.
In order to solve the application problems facing traditional industrial robots such as difficulty in identifying complex industrial parts and singleness in recognition, a visual recognition and sorting strategy based on transfer learning is proposed. Firstly, the pictures taken by high-precision industrial camera are processed by Haclon software, such as expansion, corrosion and then imported to Pytorch which has prepared a good neural network model to identify the target classification, and finally the purpose of industrial robot sorting is achieved. In the experiment, two kinds of mushrooms with varied shapes are taken as an example to implement the transfer learning and sorting on the UR5 robot platform. The experimental results illustrate the good accuracy and stability of the proposed algorithm.
引文
[1]Hinton G E,Salakhutdinov R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[2]LeCun Y,Boser B,Denker J S,et al.Backpropagation applied to handwritten zip code recognition[J].Neural Computation,1989,1(4):541-551.
[3]Krizhevsky A,Sutskever I,Hinton G E.Image net classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2012:1097-1105.
[4]Karpathy A,Toderici G,Shetty S,et al.Large-scale video classification with convolutional neural networks[C]//Computer Vision and Pattern Recognition,2014:1725-1732.
[5]杜学丹,蔡莹皓,鲁涛,等.一种基于深度学习的机械臂抓取方法[J].机器人,2017,39(6).
[6]伍锡如,黄国明,孙立宁.基于深度学习的工业分拣机器人快速视觉识别与定位算法[J].机器人,2016,38(6):711-719.
[7]Johns E,Leutenegger S,Davison A J.Deep learning a grasp function for grasping under gripper pose uncertainty[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Piscataway,USA:IEEE,2016:4461-4468.
[8]刘焕军,王耀南.机器视觉中的图像采集技术[J].电脑与信息技术,2003,11(1).
[9]Tahmoresnezhad J,Hashemi S.Visual domain adaptation via transfer feature learning[J].Knowledge&Information Systems,2016,50(2):1-21.
[10]Mahmud M M H.On universal transfer learning[C]//International Conference on Algorithmic Learning Theory.Berlin:Springer-Verlag,2007:135-149.
[11]Oyen D,Lane T.Transfer learning for Bayesian discovery of multiple Bayesian networks[M].New York:SpringerVerlag,2015.
[12]Bel N,Koster C H A,Villegas M.Cross-lingual text categorization[J].Lecture Notes in Computer Science,2003,18(2769):126-139.
[13]Yang L,Zhang J.Automatic transfer learning for short text mining[J].EURASIP Journal on Wireless Communications&Networking,2017(1):42.
[14]Dai W,Yang Q,Xue G R,et al.Boosting for transfer learning[C]//International Conference on Machine Learning,2007:193-200.
[15]Chen Y,Meng H,Wen X,et al.Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks[J].EURASIP Journal on Wireless Communications&Networking,2018(1):127.
[16]Dai W,Chen Y,Xue G R,et al.Translated learning:transfer learning across different feature spaces[C]//International Conference on Neural Information Processing Systems,2008:353-360.
[17]Wei F,Zhang J,Yan C,et al.FSFP:transfer learning from long texts to the short[J].Applied Mathematics&Information Sciences,2014,8(4):2033-2040.
[18]庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.
[19]Gao J,Fan W,Sun Y,et al.Heterogeneous source consensus learning via decision propagation and negotiation[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2009:339-348.
[20]Pan S J,Yang Q.A survey on transfer learning[J].IEEETransactions on Knowledge&Data Engineering,2010,22(10):1345-1359.
[21]孟佩,曹菡,师军.基于Softmax回归模型的协同过滤算法研究与应用[J].计算机技术与发展,2016,26(12):153-155.
[2]LeCun Y,Boser B,Denker J S,et al.Backpropagation applied to handwritten zip code recognition[J].Neural Computation,1989,1(4):541-551.
[3]Krizhevsky A,Sutskever I,Hinton G E.Image net classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2012:1097-1105.
[4]Karpathy A,Toderici G,Shetty S,et al.Large-scale video classification with convolutional neural networks[C]//Computer Vision and Pattern Recognition,2014:1725-1732.
[5]杜学丹,蔡莹皓,鲁涛,等.一种基于深度学习的机械臂抓取方法[J].机器人,2017,39(6).
[6]伍锡如,黄国明,孙立宁.基于深度学习的工业分拣机器人快速视觉识别与定位算法[J].机器人,2016,38(6):711-719.
[7]Johns E,Leutenegger S,Davison A J.Deep learning a grasp function for grasping under gripper pose uncertainty[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Piscataway,USA:IEEE,2016:4461-4468.
[8]刘焕军,王耀南.机器视觉中的图像采集技术[J].电脑与信息技术,2003,11(1).
[9]Tahmoresnezhad J,Hashemi S.Visual domain adaptation via transfer feature learning[J].Knowledge&Information Systems,2016,50(2):1-21.
[10]Mahmud M M H.On universal transfer learning[C]//International Conference on Algorithmic Learning Theory.Berlin:Springer-Verlag,2007:135-149.
[11]Oyen D,Lane T.Transfer learning for Bayesian discovery of multiple Bayesian networks[M].New York:SpringerVerlag,2015.
[12]Bel N,Koster C H A,Villegas M.Cross-lingual text categorization[J].Lecture Notes in Computer Science,2003,18(2769):126-139.
[13]Yang L,Zhang J.Automatic transfer learning for short text mining[J].EURASIP Journal on Wireless Communications&Networking,2017(1):42.
[14]Dai W,Yang Q,Xue G R,et al.Boosting for transfer learning[C]//International Conference on Machine Learning,2007:193-200.
[15]Chen Y,Meng H,Wen X,et al.Classification methods of a small sample target object in the sky based on the higher layer visualizing feature and transfer learning deep networks[J].EURASIP Journal on Wireless Communications&Networking,2018(1):127.
[16]Dai W,Chen Y,Xue G R,et al.Translated learning:transfer learning across different feature spaces[C]//International Conference on Neural Information Processing Systems,2008:353-360.
[17]Wei F,Zhang J,Yan C,et al.FSFP:transfer learning from long texts to the short[J].Applied Mathematics&Information Sciences,2014,8(4):2033-2040.
[18]庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.
[19]Gao J,Fan W,Sun Y,et al.Heterogeneous source consensus learning via decision propagation and negotiation[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2009:339-348.
[20]Pan S J,Yang Q.A survey on transfer learning[J].IEEETransactions on Knowledge&Data Engineering,2010,22(10):1345-1359.
[21]孟佩,曹菡,师军.基于Softmax回归模型的协同过滤算法研究与应用[J].计算机技术与发展,2016,26(12):153-155.