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By: Swarnali Kundu, Biswasri Datta, Tousif Parvej, Pritam Pal, and Fakruddin Ali Ahmed.
1-4 Student, Department of Information Technology, B. P. Poddar Institute of Management & Technology, Kolkata, West Bengal, India
5 Assistant Professor, Department of Information Technology, B. P. Poddar Institute of Management & Technology, Kolkata, West Bengal, India
Convolutional Neural Networks (CNNs) have become a foundational deep learning framework in computer vision because they can automatically extract layered, increasingly complex features directly from raw image data. CNNs use convolutional, pooling, and activation layers to extract spatial patterns from basic edges to intricate object components, drawing inspiration from the human visual cortex. An overview of CNNs’ basic architecture is given in this study, along with an explanation of important elements such kernels, convolution processes, pooling strategies, activation functions, and fully connected layers. To show how deep learning architectures have evolved in terms of depth, computational efficiency, and feature extraction capabilities, classic CNN models such as LeNet, AlexNet, VGGNet, and GoogLeNet are examined. Advances in large-scale picture categorization and recognition problems have been greatly aided by these structures. CNNs still have drawbacks despite their effectiveness, including high processing costs, the need for large labeled datasets, and interpretability issues. In order to serve real-time and embedded applications, future developments are anticipated to concentrate on lightweight architectures, increased model transparency, and improved training efficiency. All things considered, CNNs continue to be crucial to contemporary artificial intelligence research because they let machines to process and comprehend visual data with ever-increasing precision and resilience
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