i-manager Publications

State-of-the-Art Deep Learning Techniques for Object Identification in Practical Applications

Kanthirekha Miriyala*, Padmaja Rani B.**

*-** Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India.

Periodicity:July - December'2024
DOI : https://doi.org/10.26634/jpr.11.2.21475

Abstract

Unprecedented advancements have been witnessed in deep learning, particularly in the domain of real-world object detection. The sophisticated capabilities of contemporary deep learning methodologies to extract and process features from intricate data have catalyzed their adoption across diverse fields, including computer vision, robotics, and autonomous systems. This comprehensive literature survey examines cutting-edge deep learning approaches employed for effective real-world object detection. The review encompasses various aspects of object identification, such as detection, recognition, and segmentation, while exploring diverse deep learning frameworks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs). Furthermore, the survey provides a thorough examination of current research developments, addressing critical challenges and limitations, while identifying promising avenues for future research endeavors. The review offers valuable insights into state-of-the-art techniques and their potential real-world applications.

Keywords

Convolutional Neural Networks (CNNs), Region-based CNNs, SSD, YOLOv3, Faster R-CNN, Retina Net.

How to Cite this Article?

Miriyala, K., and Rani, B. P. (2024). State-of-the-Art Deep Learning Techniques for Object Identification in Practical Applications. i-manager’s Journal on Pattern Recognition, 11(2), 31-45. https://doi.org/10.26634/jpr.11.2.21475

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State-of-the-Art Deep Learning Techniques for Object Identification in Practical Applications

Abstract

Keywords

How to Cite this Article?

References

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