The Evolution of Convolutional Neural Networks: Progress, Innovations, and Future Directions

Abstract

Convolutional Neural Networks (CNNs) have emerged as a cornerstone of modern deep learning, with their origins rooted in biophysical models inspired by the visual system's neural mechanisms. Since the inception of early models like LeNet-5, CNNs have achieved remarkable advancements in image classification, recognition, and target localization, demonstrating their versatility and efficacy across diverse fields. This paper traces the developmental trajectory of CNNs, from the foundational principles of deep learning to the latest structural innovations exemplified by models such as AlexNet, ResNet, and DenseNet. The study also examines lightweight architectures like MobileNet and ShuffleNet, designed for deployment in resource-constrained environments. Key conceptual advancements, such as network-in-network structures and new performance-enhancing elements, are highlighted. Despite these advancements, significant challenges remain, including the computational overhead of training and the optimization of hyperparameters like convolutional kernels, depth, and learning rates. This review underscores the ongoing need for research to refine CNN architectures and ensure their efficient application in both academic and industrial settings.