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Fuzzy-Neural Approaches for Real-Time Object Detection in Computer Vision: A Comprehensive Review


EOI: 10.11242/viva-tech.01.08.039

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Citation

Vivek Sapkale, Dev Kumar, Akash Yadav, Sahil Dubey, Sheetal Solanki" Fuzzy-Neural Approaches for Real-Time Object Detection in Computer Vision: A Comprehensive Review", VIVA-IJRI Volume 1, Issue 7, Article 30, pp. 1-7, 2025. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

" Real-time object detection plays a vital role in computer vision, finding applications in areas such as self-driving vehicles and security monitoring systems. Fuzzy logic and neural networks have emerged as powerful tools for addressing challenges like uncertainty, noise, and computational complexity in object detection. This paper presents an in-depth analysis of the latest developments in fuzzy-neural approaches for real-time object detection. We analyse methodologies, applications, and performance metrics from state-of-the-art studies, highlighting the synergy between fuzzy logic and neural networks. Key challenges, such as computational complexity and dataset bias, are discussed, along with future directions, including explainable AI and edge computing This review is intended to be a useful reference for both researchers and professionals in the field"

Keywords

Computer Vision, Edge Computing, Explainable AI, Fuzzy Logic, Hybrid Systems, Neural Networks, Real-Time Object Detection

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