REVIEW OF CONVOLUTIONAL NEURAL NETWORK MODEL FOR OBJECT DETECTION, LOCALIZATION AND CLASSIFICATION

Authors
  • I. O. Adejumobi

    A

Keywords:
YOLO, Convolutional blocks, Machine learning, Localization, Detection, Classification
Abstract

Machine learning Algorithms have been used as central components in contemporary technological design and image processing to identify, locate, detect and classify items. Deep learning algorithms and data structures can be a powerful tool for this. This paper reviews several deep learning algorithms using convolutional neural networks (CNNs) blocks to Detect, Classify and Localize objects. Various convolutional architectures are elaborated, detailing how certain configurations of convolutional blocks affect performance in detection, as well as strengths and weaknesses. This study aims to provide insight into the use of various CNN models which guide developers in selecting and developing effective models or algorithms for automatic detection, localization and classification systems.

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2026-03-03
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How to Cite

REVIEW OF CONVOLUTIONAL NEURAL NETWORK MODEL FOR OBJECT DETECTION, LOCALIZATION AND CLASSIFICATION. (2026). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 20(Special), 25-34. https://doi.org/10.51459/futajeet.2026.20.Special.492

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