Research on Lightweight Object Detection and Image Recognition Based on Convolutional Neural Networks

With the rapid development of deep learning technology, Convolutional Neural Networks (CNNs) have achieved remarkable success in the field of computer vision. However, traditional CNN models often feature complex network structures and a large number of parameters, which limits their application on resource-constrained devices such as mobile devices and embedded systems. Therefore, researching lightweight convolutional neural networks is of great significance for promoting the popularization of computer vision technology on these devices. This paper first outlines the research background and significance of lightweight convolutional neural networks, and then introduces in detail the MobileNet series models, the RepViT model, and a lightweight convolutional neural network called FastEdgeCNN for image object recognition. Finally, experiments are conducted to verify the effectiveness of these lightweight models in object detection and image recognition tasks.