Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification
The fusion of Internet of Things (IoT) and deep learning (DL) methods has proven valuable in automating vehicle detection and classification tasks on remote sensing images (RSI). This technology has broad applications, including traffic monitoring, urban planning, and transportation management. Rece...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447923004367 |
| _version_ | 1797449503902531584 |
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| author | Mohammed Aljebreen Bayan Alabduallah Hany Mahgoub Randa Allafi Manar Ahmed Hamza Sara Saadeldeen Ibrahim Ishfaq Yaseen Mohamed Ibrahim Alsaid |
| author_facet | Mohammed Aljebreen Bayan Alabduallah Hany Mahgoub Randa Allafi Manar Ahmed Hamza Sara Saadeldeen Ibrahim Ishfaq Yaseen Mohamed Ibrahim Alsaid |
| author_sort | Mohammed Aljebreen |
| collection | DOAJ |
| description | The fusion of Internet of Things (IoT) and deep learning (DL) methods has proven valuable in automating vehicle detection and classification tasks on remote sensing images (RSI). This technology has broad applications, including traffic monitoring, urban planning, and transportation management. Recent advancements have demonstrated the efficacy of DL models like convolutional neural networks (CNN) in RSI classification tasks. In this aspect, this study proposes a novel honey badger optimization algorithm with an ensemble learning-based vehicle detection and classification (HBOAEL-VDC) technique. The purpose of the study is to design ensemble DL models for accurate vehicle identification and classification processes. To accomplish this, the HBOAEL-VDC technique makes use of an improved RetinaNet model for the detection of objects, i.e., vehicles on the RSI. Moreover, the classification of detected vehicles takes place using the ensemble learning process, comprising three DL models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). Furthermore, the HBOA-based parameter tuning process gets carried out to adjust the hyperparameter values of the DL models and thereby improve the classification results. The simulation outcome of the HBOAEL-VDC approach is tested on benchmark RSI databases. The experimentation outcomes reported the enhanced vehicle classification performance of the HBOAEL-VDC approach over other recent DL models. |
| first_indexed | 2024-03-09T14:25:55Z |
| format | Article |
| id | doaj.art-8b1e54fc2c4c4764b442c24bb216eb84 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-03-09T14:25:55Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-8b1e54fc2c4c4764b442c24bb216eb842023-11-28T07:25:46ZengElsevierAin Shams Engineering Journal2090-44792023-11-011411102547Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classificationMohammed Aljebreen0Bayan Alabduallah1Hany Mahgoub2Randa Allafi3Manar Ahmed Hamza4Sara Saadeldeen Ibrahim5Ishfaq Yaseen6Mohamed Ibrahim Alsaid7Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; Corresponding author.Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi ArabiaDepartment of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaThe fusion of Internet of Things (IoT) and deep learning (DL) methods has proven valuable in automating vehicle detection and classification tasks on remote sensing images (RSI). This technology has broad applications, including traffic monitoring, urban planning, and transportation management. Recent advancements have demonstrated the efficacy of DL models like convolutional neural networks (CNN) in RSI classification tasks. In this aspect, this study proposes a novel honey badger optimization algorithm with an ensemble learning-based vehicle detection and classification (HBOAEL-VDC) technique. The purpose of the study is to design ensemble DL models for accurate vehicle identification and classification processes. To accomplish this, the HBOAEL-VDC technique makes use of an improved RetinaNet model for the detection of objects, i.e., vehicles on the RSI. Moreover, the classification of detected vehicles takes place using the ensemble learning process, comprising three DL models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). Furthermore, the HBOA-based parameter tuning process gets carried out to adjust the hyperparameter values of the DL models and thereby improve the classification results. The simulation outcome of the HBOAEL-VDC approach is tested on benchmark RSI databases. The experimentation outcomes reported the enhanced vehicle classification performance of the HBOAEL-VDC approach over other recent DL models.http://www.sciencedirect.com/science/article/pii/S2090447923004367Smart environmentInternet of ThingsEnsemble learningVehicle detection |
| spellingShingle | Mohammed Aljebreen Bayan Alabduallah Hany Mahgoub Randa Allafi Manar Ahmed Hamza Sara Saadeldeen Ibrahim Ishfaq Yaseen Mohamed Ibrahim Alsaid Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification Ain Shams Engineering Journal Smart environment Internet of Things Ensemble learning Vehicle detection |
| title | Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification |
| title_full | Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification |
| title_fullStr | Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification |
| title_full_unstemmed | Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification |
| title_short | Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification |
| title_sort | integrating iot and honey badger algorithm based ensemble learning for accurate vehicle detection and classification |
| topic | Smart environment Internet of Things Ensemble learning Vehicle detection |
| url | http://www.sciencedirect.com/science/article/pii/S2090447923004367 |
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