Intelligent Visual Place Recognition using Sparrow Search Algorithm with Deep Transfer Learning Model
Intelligent Visual Place Recognition using Sparrow Search Algorithm with Deep Transfer Learning Model |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-4 |
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| Year of Publication : 2023 | ||
| Author : S. Senthamizhselvi, A. Saravanan |
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| DOI : 10.14445/22315381/IJETT-V71I4P210 | ||
How to Cite?
S. Senthamizhselvi, A. Saravanan, "Intelligent Visual Place Recognition using Sparrow Search Algorithm with Deep Transfer Learning Model," International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 109-118, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P210
Abstract
The application of Deep Learnings (DLs) is thriving in the domain of Visual Place Recognitions (VPRs) that serves an indispensable role in visual Simultaneous Localization and Mapping (vSLAM) applications. The usage of Convolutional Neural Networks (CNNs) attains superior performance compared to handcrafted feature descriptors. However, still, VPR is a difficult task because of the two major issues they are perceptual variability and perceptual aliasing. This study develops an Intelligent Visual Place Recognition using Sparrow Search Algorithm with Deep Transfer Learning (IVPR-SSADTL) model. The presented IVPR-SSADTL technique recognizes the visual places effectively and accurately. It involves a three-phase process: feature extraction, hyperparameter tuning, and place recognition. At the initial phase, the IVPR-SSADTL technique employs the MixNet model as a feature extractor with the sparrow search algorithm (SSA) as a hyperparameter optimizer. Next, in the later phase, the IVPR-SSADTL technique applies Manhattan distance-based similarity measurement to recognize the places promptly. To exhibit the higher performance of the IVPR-SSADTL system, an extensive range of simulations were performed. A wide range of comparison studies stated the improved achievement of the IVPR-SSADTL algorithm over other models.
Keywords
Visual places recognition, Transfer learning, Deep learning, MixNet model, Feature extraction, Sparrow search algorithm.
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