EXPLOITING REMOTE SENSING IMAGERY FOR VEHICLE DETECTION AND CLASSIFICATION USING ROBUST COMPETITIVE ALGORITHM WITH DEEP CONVOLUTIONAL NEURAL NETWORK

Kamal M. Othman, Hessa Alfraihi, Nadhem Nemri, Achraf Ben Miled, Nuha Alruwais and Ahmed Mahmud
Additional contact information
Kamal M. Othman: Department of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia
Hessa Alfraihi: ��Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Nadhem Nemri: ��Department of Information System, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia
Achraf Ben Miled: �Department of Computer Science at College of Science, Northern Border University, Arar, Saudi Arabia
Nuha Alruwais: �Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P. O. Box 22459, Riyadh 11495, Saudi Arabia
Ahmed Mahmud: ��Research Center, Future University in Egypt, New Cairo 11835, Egypt

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-16

Abstract: Fast and robust vehicle recognition from remote sensing images (RSIs) has excellent economic analysis, emergency management, and traffic surveillance applications. Additionally, vehicle density and location data are vital for intelligent transportation systems. However, correct and robust vehicle recognition in RSIs has been complex. Conventional vehicle recognition approaches depend on handcrafted extracted features in sliding windows with distinct scales. Recently, the convolutional neural network can be executed for aerial image object recognition, and it has accomplished promising outcomes. This research projects an automatic vehicle detection and classification utilizing an imperialist competitive algorithm with a deep convolutional neural network (VDC-ICADCNN) technique. The primary purpose of the VDC-ICADCNN technique is to develop the RSI and apply deep learning (DL) models for the recognition and identification of vehicles. Three main procedures were involved in the presented VDC-ICADCNN technique. At the primary stage, the VDC-ICADCNN technique employs the EfficientNetB7 approach for the feature extractor. Then, the hyperparameter fine-tuning of the EfficientNet approach takes place utilizing ICA, which aids in attaining improved performance in the classification process. The VDC-ICADCNN technique utilizes a variational autoencoder (VAE) model for the vehicle recognition method. Extensive experiments can be implemented to establish the superior solution of the VDC-ICADCNN technique. The obtained outcomes of the VDC-ICADCNN technique highlighted a superior accuracy value of 96.77% and 98.59% with other DL approaches under Vehicle Detection in Aerial Imagery (VEDAI) and Potsdam datasets.

Keywords: Remote Sensing; Vehicle Detection; Target Detection; Deep Learning; Machine Learning; Computer Vision (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400031

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