Revolutionizing image recognition: The dominance and future potential of convolutional neural networks

Abstract

As AI technology advances swiftly and diverse industries increasingly require image processing, traditional image recognition methods are displaying their limitations. This paper explores the evolving landscape of AI technology in the context of image processing and highlights the limitations of traditional image recognition methods. With the proliferation of big data and the evolution of deep learning, convolutional neural networks (CNNs) have emerged as a dominant solution for image recognition across diverse industries. The paper begins by elucidating the architecture of CNNs and introduces commonly employed traditional CNN models. Furthermore, it offers practical insights into the application of CNNs within various industries, illuminating the path for future CNN development. The transformative potential of CNNs is underscored, as they possess the ability to extract intricate patterns from images, reshaping numerous domains. The paper's primary focus is on CNNs in the realm of image recognition, encompassing efforts to enhance precision and efficiency in CNN-based image recognition, as well as addressing real-world challenges in this domain using CNNs.