Construction of Mathematical Model of Image Recognition Based on Deep Learning

This article explores the methodology for developing an image recognition mathematical model utilizing deep learning techniques. Architectures like Convolutional Neural Networks (CNN) have demonstrated substantial superiority in image recognition tasks, capable of autonomously learning image characteristics while enhancing both the precision and efficacy of recognition processes. The research introduces an innovative CNN architecture referred to as Enhanced-ResNet. By incorporating an attention mechanism, employing a multi-scale feature integration approach, and refining the loss function, the model's capacity to focus on critical image details, feature hierarchy, and generalizability is significantly bolstered. Experimental evaluations were conducted using the CIFAR-10, CIFAR-100, and ImageNet datasets. The results show that the Enhanced-ResNet is superior to the original ResNet and other advanced image recognition models in key performance indicators such as accuracy, precision and F1 score, especially in CIFAR-10 data set, with an accuracy of 93%. This study provides an efficient and accurate mathematical model for the field of image recognition, and has broad application prospects.