Application of computer vision algorithm based on deep learning in image recognition

In this study, we propose a new method for image recognition based on computer vision, combining deep convolutional neural networks (DCNN), attention mechanisms, and generative adversarial networks (GAN) to enhance the accuracy of object identification. First, DCNN was utilized to extract features from images to automatically identify objects within them. To better capture image features at different scales, a multi-scale attention mechanism was introduced, which significantly improves the accuracy of feature extraction by focusing on key areas in the image. Additionally, GAN-generated image data was used to augment the training dataset, thereby enhancing the model’s generalization capability when dealing with diverse real-world scenarios. Experimental results demonstrate that the proposed algorithm achieves high precision across multiple public datasets, particularly excelling in complex scenes by notably reducing false and missed detections. Compared with traditional methods, this approach improves recognition accuracy by approximately $9 \%$, providing robust technical support for rapid and precise image recognition tasks.