Object Detection Using Deep Convolutional Generative Adversarial Networks Embedded Single Shot Detector with Hyper-parameter Optimization

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Abstract

It is a challenging task to identify optimal network configurations for large-scale deep neural networks with cascaded structures. In this research, we propose a hybrid end-to-end model by integrating Deep Convolutional Generative Adversarial Network (DCGAN) with Single Shot Detector (SSD), for undertaking object detection. We subsequently employ the Particle Swarm Optimization (PSO) algorithm to conduct hyperparameter identification for the DCGAN-SSD model. The detected class labels as well as salient regional features are then used as inputs for a Long Short-Term Memory (LSTM) network for image description generation. Evaluated with a video data set in the wild, the empirical results indicate the efficiency of the proposed PSO-enhanced DCGAN-SSD object detector with respect to object detection and image description generation.

Original languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence SSCI Proceedings
Place of PublicationPiscataway, US
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728190488
ISBN (Print)9781728190495
DOIs
Publication statusPublished - 5 Dec 2021
Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
Duration: 5 Dec 20217 Dec 2021

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/TerritoryUnited States
CityOrlando
Period5/12/217/12/21

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