Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning
Recently, consumer product recognition comprises harnessing cutting-edge technologies namely artificial intelligence (AI) and computer vision (CV) to develop the purchasing expedition. This technology allows retailers to utilize robust product recognition systems that precisely identify and categori...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10528273/ |
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| author | Mohammed Alghamdi Hanan Abdullah Mengash Mohammed Aljebreen Mohammed Maray Abdulbasit A. Darem Ahmed S. Salama |
| author_facet | Mohammed Alghamdi Hanan Abdullah Mengash Mohammed Aljebreen Mohammed Maray Abdulbasit A. Darem Ahmed S. Salama |
| author_sort | Mohammed Alghamdi |
| collection | DOAJ |
| description | Recently, consumer product recognition comprises harnessing cutting-edge technologies namely artificial intelligence (AI) and computer vision (CV) to develop the purchasing expedition. This technology allows retailers to utilize robust product recognition systems that precisely identify and categorize products in real time. The comprehension of automatic product identification becomes of major importance for both social and economic improvement since it is more reliable and time-consuming than manual function. Product detection through images is a complex task in the domain of CV. This can be obtained the improving consideration because of the excellent application viewpoint like visually impaired assistance, stock tracking, automatic checkout, and planogram compliance. Currently, deep learning (DL) prefers a successful progression with great achievements in object detection and image classification. Therefore, this study presents Advanced Consumer Product Recognition using the Aquila Optimization Algorithm with Deep Learning (ACPR-AOADL) technique. The proposed ACPR-AOADL model utilizes hyperparameter-tuned DL concepts for the identification of consumer products. To achieve this, the ACPR-AOADL model first pre-processes the input data utilizing a Wiener filter (WF) to improve the image quality. Besides, the YOLO-v8 model with a deep residual network (DRN) as a backbone network can be applied for the product detection process. For product classification, the deep belief network (DBN) approach can be used. To boost the complete product detection process, the ACPR-AOADL technique involves AOA based hyperparameter selection process. The performance analysis of the ACPR-AOADL method can be examined under the Product-10K dataset. Wide-ranging results stated that the ACPR-AOADL technique reaches enhanced classification performance over other compared approaches. |
| format | Article |
| id | doaj-art-93f1b0aebe5b46c7b697cffada6d106c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-93f1b0aebe5b46c7b697cffada6d106c2025-08-20T03:10:07ZengIEEEIEEE Access2169-35362024-01-0112710557106510.1109/ACCESS.2024.339948010528273Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep LearningMohammed Alghamdi0https://orcid.org/0000-0002-5042-0690Hanan Abdullah Mengash1https://orcid.org/0000-0002-4103-2434Mohammed Aljebreen2Mohammed Maray3https://orcid.org/0000-0002-7066-2945Abdulbasit A. Darem4https://orcid.org/0000-0002-5650-1838Ahmed S. Salama5https://orcid.org/0000-0002-1066-8261Department of Information and Technology Systems, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Computer Science, Community College, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science, College of Science, Northern Border University, Arar, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, EgyptRecently, consumer product recognition comprises harnessing cutting-edge technologies namely artificial intelligence (AI) and computer vision (CV) to develop the purchasing expedition. This technology allows retailers to utilize robust product recognition systems that precisely identify and categorize products in real time. The comprehension of automatic product identification becomes of major importance for both social and economic improvement since it is more reliable and time-consuming than manual function. Product detection through images is a complex task in the domain of CV. This can be obtained the improving consideration because of the excellent application viewpoint like visually impaired assistance, stock tracking, automatic checkout, and planogram compliance. Currently, deep learning (DL) prefers a successful progression with great achievements in object detection and image classification. Therefore, this study presents Advanced Consumer Product Recognition using the Aquila Optimization Algorithm with Deep Learning (ACPR-AOADL) technique. The proposed ACPR-AOADL model utilizes hyperparameter-tuned DL concepts for the identification of consumer products. To achieve this, the ACPR-AOADL model first pre-processes the input data utilizing a Wiener filter (WF) to improve the image quality. Besides, the YOLO-v8 model with a deep residual network (DRN) as a backbone network can be applied for the product detection process. For product classification, the deep belief network (DBN) approach can be used. To boost the complete product detection process, the ACPR-AOADL technique involves AOA based hyperparameter selection process. The performance analysis of the ACPR-AOADL method can be examined under the Product-10K dataset. Wide-ranging results stated that the ACPR-AOADL technique reaches enhanced classification performance over other compared approaches.https://ieeexplore.ieee.org/document/10528273/Product recognitionAquila optimization algorithmdeep learningWiener filtercomputer vision |
| spellingShingle | Mohammed Alghamdi Hanan Abdullah Mengash Mohammed Aljebreen Mohammed Maray Abdulbasit A. Darem Ahmed S. Salama Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning IEEE Access Product recognition Aquila optimization algorithm deep learning Wiener filter computer vision |
| title | Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning |
| title_full | Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning |
| title_fullStr | Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning |
| title_full_unstemmed | Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning |
| title_short | Empowering Retail Through Advanced Consumer Product Recognition Using Aquila Optimization Algorithm With Deep Learning |
| title_sort | empowering retail through advanced consumer product recognition using aquila optimization algorithm with deep learning |
| topic | Product recognition Aquila optimization algorithm deep learning Wiener filter computer vision |
| url | https://ieeexplore.ieee.org/document/10528273/ |
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