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Parallel Convolutional Neural Networks for Object Detection

Adedeji Olugboja 1, Zenghui Wang 1, and Yanxia Sun 2
1. College of Science, Engineering and Technology, University of South Africa, Johannesburg, South Africa
2. Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa

Abstract—In recent years, Convolutional Neural Network (CNN) has been widely applied in speech/image/video recognition and classification. Although the results achieved are so impressive, CNN architecture is becoming more and more complex since CNN includes more layers to achieve better performance. In this paper, we developed a new CNN structure with several parallel CNNs and a Back-Propagation Neural Network (BPNN). The parallel CNNs can have the same or different numbers of layers. The outputs of the CNNs are the inputs of a fully connected BPNN. The structure of the proposed model can reduce the complexity of CNN by reducing the total number of CNN layers while the performance of feature extraction can be improved. The proposed model was validated based on CIFAR-10, CIFAR-100, and MNIST datasets and the achieved performance of the model is promising.
Index Terms—convolution neural network, back-propagation neural network, feature extraction, visualization, object detection

Cite: Adedeji Olugboja, Zenghui Wang, and Yanxia Sun, "Parallel Convolutional Neural Networks for Object Detection," Journal of Advances in Information Technology, Vol. 12, No. 4, pp. 279-286, November 2021. doi: 10.12720/jait.12.4.279-286

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.