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Nonlinear dimensionality reduction in robot vision for industrial monitoring process via deep three dimensional Spearman correlation analysis (D3D-SCA)

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Abstract

During the era of Industry 4.0, the industrial robot monitoring process is getting success and popularity day by day. It also plays a vital role in the enhancement of robot vision algorithms. This paper proposed a model Deep Three Dimensional Spearman Correlation Analysis (D3D-SCA) to address nonlinear dimensionality reduction in robot vision for three-dimensional data. Dealing with three-dimensional multimedia datasets using traditional algorithms, to date, researchers have been facing limitations and challenges because mostly sub-space learning algorithms and their developments cannot perform satisfactorily in most of the time with linear and non-linear data dependency. The proposed model directly finds the relations between two sets of three-dimensional data without reshaping the data into 2D-matrices or vectors and dramatically reduces the dimensional reduction and computational algorithm complexity. The proposed model extracts deep information and translates it into a decision. To do so, three components are employed in the proposed model: customized deep learning model Inception_V3 for deep feature mapping, three-dimensional spearman correlation analysis for comparing pairwise deep features without a singular matrix and spatial dilemma problem, and the customized Xception classifier with automatic online updating ability and adjustable neural architecture for low latency models. The motivation of the proposed model is to advance the scalability of existing industrial robot vision applications which based on recognition, detection and re-identification approaches. Extensive findings on industrial datasets named “3D Objects on turntable and Caltech 101” demonstrate the effectiveness of the proposed model.

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Acknowledgments

This research is supported by National Natural Science Foundation of China (61972183, 61672268) and National Engineering Laboratory Director Foundation of Big Data Application for Social Security Risk Perception and Prevention.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Keyang Cheng & Muhammad Saddam Khokhar

  2. School of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Misbah Ayoub

  3. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Zakria Jamali

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  1. Keyang Cheng
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  2. Muhammad Saddam Khokhar
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  3. Misbah Ayoub
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  4. Zakria Jamali
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Correspondence to Muhammad Saddam Khokhar.

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Cheng, K., Khokhar, M.S., Ayoub, M. et al. Nonlinear dimensionality reduction in robot vision for industrial monitoring process via deep three dimensional Spearman correlation analysis (D3D-SCA). Multimed Tools Appl 80, 5997–6017 (2021). https://doi.org/10.1007/s11042-020-09859-6

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