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.

在工业4.0时代，工业机器人监控过程日益获得成功和普及。它在增强机器人视觉算法中也起着至关重要的作用。本文提出了一个模型“深三维三维斯皮尔曼相关分析（D3D-SCA）”，以解决三维图像中机器人视觉中的非线性降维问题。迄今为止，由于使用传统算法处理三维多媒体数据集，研究人员一直面临局限和挑战，因为大多数子空间学习算法及其发展在大多数情况下不能令人满意地满足线性和非线性数据依赖。所提出的模型直接找到两组三维数据之间的关系，而无需将数据重新整形为2D矩阵或矢量，从而大大减少了维数缩减和计算算法的复杂性。所提出的模型提取深层信息并将其转换为决策。为此，在建议的模型中采用了三个组成部分：用于深度特征映射的定制深度学习模型Inception_V3，用于比较成对的深度特征而没有奇异矩阵和空间难题的三维Spearman相关分析以及具有自动特征的定制Xception分类器在线更新能力和可调整的神经体系结构，适用于低延迟模型。提出的模型的动机是基于识别，检测和重新识别方法来提高现有工业机器人视觉应用程序的可伸缩性。在名为“转盘和Caltech 101上的3D对象”的工业数据集上的大量发现证明了该模型的有效性。