In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, attention, depthwise convolutions, and NAS, among others. Each type of CNN has its unique structure and characteristics, making it suitable for specific tasks. It's crucial to gain a thorough understanding and perform a comparative analysis of these different CNN types to understand their strengths and weaknesses. Furthermore, studying the performance, limitations, and practical applications of each type of CNN can aid in the development of new and improved architectures in the future. We also dive into the platforms and frameworks that researchers utilize for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences and emphasizing their respective advantages, disadvantages, applications, challenges, and future trends.

翻译：在当今数字时代，作为深度学习子集的卷积神经网络被广泛应用于图像分类、目标检测和图像分割等各类计算机视觉任务。为满足特定需求，研究者设计了包括1D、2D和3D卷积神经网络，以及空洞卷积、分组卷积、注意力卷积、深度可分离卷积和神经架构搜索在内的多种网络类型。每种卷积神经网络都具有独特结构和特征，适用于特定任务。深入理解并对比分析这些不同类型的CNN，对把握其优势与局限性至关重要。此外，研究各类CNN的性能、局限性和实际应用有助于未来开发更优的新型架构。我们从多维度探究了研究者用于研究或开发的平台与框架，同时探讨了CNN的主要研究领域，如6D视觉、生成模型和元学习。本综述对各类CNN架构进行全面审视与比较，重点阐释其架构差异，并强调各自在优势、不足、应用场景、面临挑战及未来发展趋势方面的特征。