Specific application of image recognition

Image recognition technology is an important branch of artificial intelligence research and one of the most widely used artificial intelligence technologies in people's daily life. In recent years, with the development of deep learning technology, the accuracy of image recognition has been significantly improved. This paper studies the traditional image recognition technology and deep learning technology, analyzes some shortcomings of deep learning technology, and gives feasible solutions in the future.

Artificial intelligence; Image recognition; Deep learning

1 overview

Image recognition technology is an important branch of artificial intelligence research. Based on images, it uses computers to process, analyze and understand images to identify objects with different patterns. At present, image recognition technology is widely used, in the field of security, there are face recognition, fingerprint recognition and so on; In the military field, there are topographic survey, flying object identification and so on. In the field of transportation, there are traffic sign recognition, license plate number recognition and so on. The research of image recognition technology is an important basis for more advanced image understanding, robotics, driverless and other technologies.

The traditional image recognition technology mainly consists of image processing, feature extraction, classifier design and other steps. Through expert design, image features are extracted, and images are identified and classified. The development of deep learning in recent years has greatly improved the accuracy of image recognition. Deep learning learns knowledge (features) from a large number of data and automatically completes the task of feature extraction and classification. However, the current deep learning technology relies too much on big data, and only with a large number of labeled training samples can we achieve better recognition results.