A Survey on the Applications of Convolutional Neural Networks in Computer Vision

Convolutional Neural Networks (CNNs) have become the dominant paradigm in computer vision since the ImageNet breakthrough, establishing a solid engineering foundation in both cloud and edge scenarios. Addressing the current status where existing reviews often focus on a single dimension and lack an integrated engineering perspective, this paper systematically reviews the architectural evolution, key components, and application practices of modern CNNs, primarily focusing on image classification. First, the paper elucidates the design principles of key components, including convolution and receptive fields (RF) , normalization and activation functions , and attention mechanisms. It then traces the evolution path of modern hybrid backbones, from AlexNet/ResNet to MobileNet/EfficientNet, and further to ConvNeXt , following a timeline of "deepening and residualization – lightweight and automated scaling – large kernels and Transformer fusion". Second, by integrating typical application scenarios such as object detection, semantic segmentation, and super-resolution , this review distills reusable training recipes and efficiency optimization strategies involving the synergistic use of pruning, quantization, and distillation. It also provides a checklist for evaluation and deployment geared toward actual hardware. Finally, the article analyzes challenges such as long-tail categories, cross-domain distribution shift, and on-device computational constraints, and looks forward to future trends in self-supervised learning, hardware-aware design, and model robustness optimization.