Object detection is responsible for classifying and locating objects in an image or video, which has become famous in recent years due to its wide applications. This paper reviews recent advances in deep learning-based object recognition. An overview of benchmark datasets and evaluation criteria used in recognition is also presented along with some of the main architectures used in the object recognition problem. Also, the modern lightweight classification models used have been reviewed. Finally, the performance of these structures has been compared on multiple criteria.
Norouzi, M. , hassanpour, H. and Ghanbari, A. (2024). Investigating object recognition models based on deep learning. Soft Computing Journal, (), -. doi: 10.22052/scj.2024.252945.1149
MLA
Norouzi, M. , , hassanpour, H. , and Ghanbari, A. . "Investigating object recognition models based on deep learning", Soft Computing Journal, , , 2024, -. doi: 10.22052/scj.2024.252945.1149
HARVARD
Norouzi, M., hassanpour, H., Ghanbari, A. (2024). 'Investigating object recognition models based on deep learning', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2024.252945.1149
CHICAGO
M. Norouzi , H. hassanpour and A. Ghanbari, "Investigating object recognition models based on deep learning," Soft Computing Journal, (2024): -, doi: 10.22052/scj.2024.252945.1149
VANCOUVER
Norouzi, M., hassanpour, H., Ghanbari, A. Investigating object recognition models based on deep learning. Soft Computing Journal, 2024; (): -. doi: 10.22052/scj.2024.252945.1149
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