Deep learning for object recognition: A comprehensive review of models and algorithms
The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | International Journal of Cognitive Computing in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266630742500004X |
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| author | Paschalis Tsirtsakis Georgios Zacharis George S. Maraslidis George F. Fragulis |
| author_facet | Paschalis Tsirtsakis Georgios Zacharis George S. Maraslidis George F. Fragulis |
| author_sort | Paschalis Tsirtsakis |
| collection | DOAJ |
| description | The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a complex domain with persistent challenges and limitations. This work seeks to address these challenges by investigating the effectiveness of deep learning (DL) methods in object detection tasks. Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction. Additionally, it evaluates the benchmark datasets commonly employed to assess their performance in object recognition tasks. |
| format | Article |
| id | doaj-art-863471311a9c460f9c6ebf2ab15141f3 |
| institution | Kabale University |
| issn | 2666-3074 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj-art-863471311a9c460f9c6ebf2ab15141f32025-01-28T04:14:55ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742025-12-016298312Deep learning for object recognition: A comprehensive review of models and algorithmsPaschalis Tsirtsakis0Georgios Zacharis1George S. Maraslidis2George F. Fragulis3Department of Electrical and Computer Engineering, University of Western Macedonia, ZEP Campus, Kozani, 50100, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, ZEP Campus, Kozani, 50100, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, ZEP Campus, Kozani, 50100, GreeceCorresponding author.; Department of Electrical and Computer Engineering, University of Western Macedonia, ZEP Campus, Kozani, 50100, GreeceThe rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a complex domain with persistent challenges and limitations. This work seeks to address these challenges by investigating the effectiveness of deep learning (DL) methods in object detection tasks. Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction. Additionally, it evaluates the benchmark datasets commonly employed to assess their performance in object recognition tasks.http://www.sciencedirect.com/science/article/pii/S266630742500004XObject recognitionComputer visionDeep learningConvolutional neural networksDatasets |
| spellingShingle | Paschalis Tsirtsakis Georgios Zacharis George S. Maraslidis George F. Fragulis Deep learning for object recognition: A comprehensive review of models and algorithms International Journal of Cognitive Computing in Engineering Object recognition Computer vision Deep learning Convolutional neural networks Datasets |
| title | Deep learning for object recognition: A comprehensive review of models and algorithms |
| title_full | Deep learning for object recognition: A comprehensive review of models and algorithms |
| title_fullStr | Deep learning for object recognition: A comprehensive review of models and algorithms |
| title_full_unstemmed | Deep learning for object recognition: A comprehensive review of models and algorithms |
| title_short | Deep learning for object recognition: A comprehensive review of models and algorithms |
| title_sort | deep learning for object recognition a comprehensive review of models and algorithms |
| topic | Object recognition Computer vision Deep learning Convolutional neural networks Datasets |
| url | http://www.sciencedirect.com/science/article/pii/S266630742500004X |
| work_keys_str_mv | AT paschalistsirtsakis deeplearningforobjectrecognitionacomprehensivereviewofmodelsandalgorithms AT georgioszacharis deeplearningforobjectrecognitionacomprehensivereviewofmodelsandalgorithms AT georgesmaraslidis deeplearningforobjectrecognitionacomprehensivereviewofmodelsandalgorithms AT georgeffragulis deeplearningforobjectrecognitionacomprehensivereviewofmodelsandalgorithms |