Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications
With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the e...
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2023-12-01
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| Series: | Baghdad Science Journal |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8177 |
| _version_ | 1797402030381203456 |
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| author | Omar Abdullatif Jassim Mohammed Jawad Abed Zenah Hadi Saied |
| author_facet | Omar Abdullatif Jassim Mohammed Jawad Abed Zenah Hadi Saied |
| author_sort | Omar Abdullatif Jassim |
| collection | DOAJ |
| description |
With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor sets, resulting in four trained models. The test sets are used to evaluate the trained models using many evaluation metrics (accuracy, TPR, FNR, PPR, FDR). Results of Google Net model indicate the high performance of the designed models with 99.34% and 99.76% accuracies for indoor and outdoor datasets, respectively. For Mobile Net models, the result accuracies are 99.27% and 99.68% for indoor and outdoor sets, respectively. The proposed methodology is compared with similar ones in the field of object recognition and image classification, and the comparative study proves the transcendence of the propsed system.
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| first_indexed | 2024-03-09T02:18:30Z |
| format | Article |
| id | doaj.art-06305e37aeaf4392bfc1bee0a9c52662 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-03-09T02:18:30Z |
| publishDate | 2023-12-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-06305e37aeaf4392bfc1bee0a9c526622023-12-06T20:00:54ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862023-12-01206(Suppl.)10.21123/bsj.2023.8177Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition ApplicationsOmar Abdullatif Jassim0Mohammed Jawad Abed1Zenah Hadi Saied2Department of Medical Instrumentation Techniques Engineering, College of Al Hikma, University, Baghdad, Iraq.Department of Medical Instrumentation Techniques Engineering, College of Al Hikma, University, Baghdad, Iraq.Department of Medical Laboratory Technologies, Institute of Medical Technology-Al-Mansour, Middle Technical University, Baghdad, Iraq. With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor sets, resulting in four trained models. The test sets are used to evaluate the trained models using many evaluation metrics (accuracy, TPR, FNR, PPR, FDR). Results of Google Net model indicate the high performance of the designed models with 99.34% and 99.76% accuracies for indoor and outdoor datasets, respectively. For Mobile Net models, the result accuracies are 99.27% and 99.68% for indoor and outdoor sets, respectively. The proposed methodology is compared with similar ones in the field of object recognition and image classification, and the comparative study proves the transcendence of the propsed system. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8177Deep learning, GoogleNet, Image classification, Indoor/outdoor, Transfer learning. |
| spellingShingle | Omar Abdullatif Jassim Mohammed Jawad Abed Zenah Hadi Saied Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications Baghdad Science Journal Deep learning, GoogleNet, Image classification, Indoor/outdoor, Transfer learning. |
| title | Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications |
| title_full | Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications |
| title_fullStr | Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications |
| title_full_unstemmed | Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications |
| title_short | Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications |
| title_sort | indoor outdoor deep learning based image classification for object recognition applications |
| topic | Deep learning, GoogleNet, Image classification, Indoor/outdoor, Transfer learning. |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8177 |
| work_keys_str_mv | AT omarabdullatifjassim indooroutdoordeeplearningbasedimageclassificationforobjectrecognitionapplications AT mohammedjawadabed indooroutdoordeeplearningbasedimageclassificationforobjectrecognitionapplications AT zenahhadisaied indooroutdoordeeplearningbasedimageclassificationforobjectrecognitionapplications |