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  Published Paper Details:

  Paper Title

A Review on Object Detection Techniques: From Traditional Methods to Deep Learning Approaches

  Authors

  Sayyed Humaira Ali

  Keywords

Object detection, computer vision, deep learning, CNN, YOLO, SSD, transformers, image processing.

  Abstract


Object detection plays a crucial role in modern computer vision, enabling applications ranging from autonomous driving and surveillance to robotics and healthcare. Traditional methods relied on handcrafted features such as Haar, HOG, and SIFT combined with classical classifiers like SVM and Adaboost. However, the advent of deep learning revolutionized object detection through Convolutional Neural Networks (CNNs) and advanced models such as the R-CNN family, YOLO, SSD, and Transformers. This review compares traditional and deep learning-based methods, summarizing their advantages, limitations, and applications while highlighting current challenges and potential future research directions.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2510276

  Paper ID - 294972

  Page Number(s) - c314-c316

  Pubished in - Volume 13 | Issue 10 | October 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Sayyed Humaira Ali,   "A Review on Object Detection Techniques: From Traditional Methods to Deep Learning Approaches", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 10, pp.c314-c316, October 2025, Available at :http://www.ijcrt.org/papers/IJCRT2510276.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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