SMRFC-PDCNN: An efficient Sense Matching Recognition with DCNN and Feature Clustering on Spark

Abstract

Sense recognition, an AI technology based on deep learning, has been widely used in public safety, road traffic, and automatic driving, but applying it on massive data in deep convolutional neural networks (DCNNs) results in performance bottlenecks. This paper proposes SMRFC-PDCNN, an efficient sense matching recognition algorithm that addresses three specific problems: decreased accuracy of feature maps, redundant feature calculations, and low efficiency in parallel recognition. The proposed algorithm includes a feature pooling selection strategy called MI-IPSS, a feature selection strategy called DCPSO-FSS, and a load balancing strategy called CCG-LBS. MI-IPSS solves the problem of decreased accuracy of feature maps by adapting the pooling strategy based on mutual information coefficient between feature maps before and after pooling. DCPSO-FSS uses density clustering and particle swarm optimization to locate clustering parameters quickly and recognize clustered features through sampling in the fully connected layer. CCG-LBS dynamically calculates the computing overhead of feature maps and allocates data between groups according to the overhead to solve the problem of low efficiency in parallel recognition. Experimental results show that SMRFC-PDCNN has good performance and is suitable for the fast sense matching recognition process of parallelized DCNN models on large-scale datasets.