Research on the Application of Computer Vision Algorithms Based on Deep Learning in Image Recognition

A new method of image recognition based on Mobile Net and Self-Attention is presented. Firstly, the structure of the network is optimized, and a multiscale feature fusion strategy is introduced to enhance the recognition capability of the model. Secondly, a method of optimizing the speed of learning based on gradient clipping is proposed, which makes it possible to avoid the loss of gradient or explosion in the training process. Moreover, by combining the data enhancement and the transfer learning strategy, the proposed method can improve the generalization capability of the model, and keep the high precision in the small sample environment. The results have been widely validated on the CIFAR-10 and ImageNet datasets. Compared with ResNet-50, the precision of the improved algorithm is 2.8% and 2.5%, and the reasoning speed is increased by 15%, and the computation cost is reduced by 20%. In addition, the Grad-CAM visualization analysis shows that the algorithm in this paper can focus on key areas more effectively and improve the effectiveness of feature extraction. Compared with traditional convolutional neural networks (such as VGG, ResNet, etc.), this research method has achieved a better balance between computational efficiency and recognition accuracy, providing a new optimization idea for the further development of computer vision, especially for scenarios with limited computing resources such as mobile devices and real-time monitoring.