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Synergizing Remora Optimization Algorithm and Transfer Learning for Visual Places Recognition in Intelligent Transportation Systems and Consumer Electronics | IEEE Journals & Magazine | IEEE Xplore

Synergizing Remora Optimization Algorithm and Transfer Learning for Visual Places Recognition in Intelligent Transportation Systems and Consumer Electronics


Abstract:

Visual Place Recognition (VPR) sits at the junction of artificial intelligence (AI) and our physical world, promising to modernize both Consumer Electronics (CE) and Inte...Show More

Abstract:

Visual Place Recognition (VPR) sits at the junction of artificial intelligence (AI) and our physical world, promising to modernize both Consumer Electronics (CE) and Intelligent Transportation Systems (ITS). VPR authorizes self-driving cars to recognize landmarks, straight difficult connections, and realize traffic signs, making independent driving securer and more effective. VPR in ITS includes the application of computer vision (CV) models to study and understand visual information from the environment, permitting transports and set up to understand and direct different surroundings. This sophisticated technique includes the identification and understanding of landmarks, road signs, and other related visual cues, simplifying present decision-making for improved navigation, traffic management, and complete system intelligence. By connecting innovative image processing models and machine learning (ML) methods, visual place detection plays an essential part in enhancing protection, efficacy, and functionality within intelligent transportation methods. Therefore, this study designs a new Remora Optimization Algorithm and Transfer Learning for Visual Places Recognition (ROATL-VPR) in the ITS environment. The ROATL-VPR technique aims to effectually and robustly detect the visual places using an optimal DL model. At the preliminary stage, the ROATL-VPR model mainly pre-processes input images using the Wiener filtering (WF) technique. For learning the complex and intrinsic patterns in the image, the ROATL-VPR technique follows the EfficientNetB3 model as feature extraction. Moreover, the hyperparameter tuning of the EfficientNetB3 method takes place by utilizing the ROA. To identify the visual places precisely, the ROATL-VPR technique applies Manhattan distance which detects the visual places proficiently. The performance validation of the ROATL-VPR method takes place by utilizing a benchmark VPR database. The experimental outcomes highlighted that the ROATL-VPR technique...
Published in: IEEE Transactions on Consumer Electronics ( Volume: 70, Issue: 1, February 2024)
Page(s): 3731 - 3739
Date of Publication: 12 March 2024

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