A Study of Deep Learning Based Computer Vision Algorithms in Image Recognition

With the rapid development of computer vision technology, image recognition, as one of its core tasks, plays an important role in several industries. However, traditional image recognition methods suffer from low computational efficiency and high storage requirements when dealing with large-scale image data. In recent years, the application of deep learning, especially convolutional neural network (CNN), has significantly improved the accuracy and efficiency of image recognition. In this paper, we propose an image recognition system that combines deep learning and hashing algorithm, which improves the effect of image feature extraction and similarity computation through the improved VGG16 model and the introduction of hashing layer. The main architecture of the system consists of an image retrieval module and an image database, which uses CNN to extract features from images and generates image “fingerprints” through hashing algorithms, accelerating the retrieval process of large-scale image data. Through comparison experiments with the traditional method, the improved system improves the average search accuracy rate (MAP) of image recognition by about 4 percentage points, and under different query conditions, the search rate (recall) also shows obvious advantages. The experimental results show that the combination of deep learning and hash-based algorithms can effectively improve the performance of the image recognition system, which has a wide range of application prospects.