Image Depth Analysis: From Deep Learning to Parallel Cluster Computing

Abstract

This research study begins with deep learning and progresses to cluster computing to complete the image depth analysis pipeline. The deep neural model is taken into account in designing the proposed model. The convolutional layer is composed of several convolutional units in morphology, and the feature value of the related image is obtained through the convolution and operation. The parallel structure is utilized to optimize this layer. Further, the original data is taken as input, and complete the construction of the proposed model through a series of operations such as convolution, pooling, and nonlinear activation function mapping. The depth image analysis is selected as the verification target. Through the simulation, the analysis accuracy has been much higher than the traditional methods.