The Research of Deep Learning Based on Visual Detection and Recognition Algorithm for Moving Object

With the rapid development of Internet of Things technology, moving object detection has become a new research field. This paper provides an overview of the algorithmic principles and processes of deep learning, and elaborates in detail on the basic principles of convolutional neural networks, including the overall framework design of the algorithm, network structure selection, and optimization strategies. Through in-depth research on mainstream algorithms for deep learning based on visual object detection and recognition, we design an algorithm suitable for object feature extraction, classifier design, model training and detection in moving. By modeling, image preprocessing can be achieved from target localization, feature extraction, target matching, and moving estimation. Convolutional neural networks is used to model the detection and recognition of moving object, analyze the performance of the algorithm in different scenarios, and point out the advantages and disadvantages of the algorithm. This paper provides a solution for moving target detection and recognition based on deep learning, and also provides technical support for the detection and recognition of people, vehicles, and road objects in related fields such as intelligent connected vehicles and intelligent transportation. This paper has certain theoretical significance and practical value.