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Deep learning for object recognition: A comprehensive review of models and algorithms

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Bibliographic Details
Title: Deep learning for object recognition: A comprehensive review of models and algorithms
Authors: Paschalis Tsirtsakis, Georgios Zacharis, George S. Maraslidis, George F. Fragulis
Source: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 298-312 (2025)
Publisher Information: Elsevier BV, 2025.
Publication Year: 2025
Subject Terms: Electronic computers. Computer science, Science, Datasets, Computer vision, Deep learning, Convolutional neural networks, Object recognition, QA75.5-76.95
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.
Document Type: Article
Language: English
ISSN: 2666-3074
DOI: 10.1016/j.ijcce.2025.01.004
Access URL: https://doaj.org/article/863471311a9c460f9c6ebf2ab15141f3
Rights: CC BY NC ND
Accession Number: edsair.doi.dedup.....f33ef54018bb0c8697b51cdc8476cdf1
Database: OpenAIRE
Description
Abstract: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.
ISSN:26663074
DOI:10.1016/j.ijcce.2025.01.004